SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

Toward semantic-based retrieval of visual information: a model-based approach

NASA Astrophysics Data System (ADS)

Park, Youngchoon; Golshani, Forouzan; Panchanathan, Sethuraman

2002-07-01

This paper center around the problem of automated visual content classification. To enable classification based image or visual object retrieval, we propose a new image representation scheme called visual context descriptor (VCD) that is a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region. VCD utilizes the predetermined quality dimensions (i.e., types of features and quantization level) and semantic model templates mined in priori. Not only observed visual cues, but also contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector (e.g., color histogram, Gabor texture, etc.,) into a discrete event (e.g., terms in text). Good-feature to track, rule of thirds, iterative k-means clustering and TSVQ are involved in transformation of feature vectors into unified symbolic representations called visual terms. Similarity-based visual cue frequency estimation is also proposed and used for ensuring the correctness of model learning and matching since sparseness of sample data causes the unstable results of frequency estimation of visual cues. The proposed method naturally allows integration of heterogeneous visual or temporal or spatial cues in a single classification or matching framework, and can be easily integrated into a semantic knowledge base such as thesaurus, and ontology. Robust semantic visual model template creation and object based image retrieval are demonstrated based on the proposed content description scheme.

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.

PubMed

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-13

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.

Deep visual-semantic for crowded video understanding

NASA Astrophysics Data System (ADS)

Deng, Chunhua; Zhang, Junwen

2018-03-01

Visual-semantic features play a vital role for crowded video understanding. Convolutional Neural Networks (CNNs) have experienced a significant breakthrough in learning representations from images. However, the learning of visualsemantic features, and how it can be effectively extracted for video analysis, still remains a challenging task. In this study, we propose a novel visual-semantic method to capture both appearance and dynamic representations. In particular, we propose a spatial context method, based on the fractional Fisher vector (FV) encoding on CNN features, which can be regarded as our main contribution. In addition, to capture temporal context information, we also applied fractional encoding method on dynamic images. Experimental results on the WWW crowed video dataset demonstrate that the proposed method outperform the state of the art.

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention

PubMed Central

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-01

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features. PMID:26759193

Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

PubMed

Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

2016-01-01

Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

Adapting to conversation with semantic dementia: using enactment as a compensatory strategy in everyday social interaction

PubMed Central

Kindell, Jacqueline; Sage, Karen; Keady, John; Wilkinson, Ray

2014-01-01

Background Studies to date in semantic dementia have examined communication in clinical or experimental settings. There is a paucity of research describing the everyday interactional skills and difficulties seen in this condition. Aims To examine the everyday conversation, at home, of an individual with semantic dementia. Methods & Procedures A 71-year-old man with semantic dementia and his wife were given a video camera and asked to record natural conversation in the home situation with no researcher present. Recordings were also made in the home environment, with the individual with semantic dementia in conversation with a member of the research team. Conversation analysis was used to transcribe and analyse the data. Recurring features were noted to identify conversational patterns. Outcomes & Results Analysis demonstrated a repeated practice by the speaker with semantic dementia of acting out a diversity of scenes (enactment). As such, the speaker regularly used direct reported speech along with paralinguistic features (such as pitch and loudness) and non-vocal communication (such as body posture, pointing and facial expression) as an adaptive strategy to communicate with others in conversation. Conclusions & Implications This case shows that while severe difficulties may be present on neuropsychological assessment, relatively effective communicative strategies may be evident in conversation. A repeated practice of enactment in conversation allowed this individual to act out, or perform what he wanted to say, allowing him to generate a greater level of meaningful communication than his limited vocabulary alone could achieve through describing the events concerned. Such spontaneously acquired adaptive strategies require further attention in both research and clinical settings in semantic dementia and analysis of interaction in this condition, using conversation analysis, may be helpful. PMID:24033649

Semantic concept-enriched dependence model for medical information retrieval.

PubMed

Choi, Sungbin; Choi, Jinwook; Yoo, Sooyoung; Kim, Heechun; Lee, Youngho

2014-02-01

In medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a semantic concept-based term-dependence feature into a formal retrieval model to improve its ranking performance. Standardized medical concept terms used by medical professionals were assumed to have implicit dependency within the same concept. We hypothesized that, by elaborately revising the ranking algorithms to favor documents that preserve those implicit dependencies, the ranking performance could be improved. The implicit dependence features are harvested from the original query using MetaMap. These semantic concept-based dependence features were incorporated into a semantic concept-enriched dependence model (SCDM). We designed four different variants of the model, with each variant having distinct characteristics in the feature formulation method. We performed leave-one-out cross validations on both a clinical document corpus (TREC Medical records track) and a medical literature corpus (OHSUMED), which are representative test collections in medical information retrieval research. Our semantic concept-enriched dependence model consistently outperformed other state-of-the-art retrieval methods. Analysis shows that the performance gain has occurred independently of the concept's explicit importance in the query. By capturing implicit knowledge with regard to the query term relationships and incorporating them into a ranking model, we could build a more robust and effective retrieval model, independent of the concept importance. Copyright © 2013 Elsevier Inc. All rights reserved.

A Sieving ANN for Emotion-Based Movie Clip Classification

NASA Astrophysics Data System (ADS)

Watanapa, Saowaluk C.; Thipakorn, Bundit; Charoenkitkarn, Nipon

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.

Automated analysis of free speech predicts psychosis onset in high-risk youths

PubMed Central

Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo A; Slezak, Diego Fernández; Sigman, Mariano; Mota, Natália B; Ribeiro, Sidarta; Javitt, Daniel C; Copelli, Mauro; Corcoran, Cheryl M

2015-01-01

Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry. PMID:27336038

Intelligent services for discovery of complex geospatial features from remote sensing imagery

NASA Astrophysics Data System (ADS)

Yue, Peng; Di, Liping; Wei, Yaxing; Han, Weiguo

2013-09-01

Remote sensing imagery has been commonly used by intelligence analysts to discover geospatial features, including complex ones. The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. The methods of extraction of elementary ground features such as buildings and roads from remote sensing imagery have been studied extensively. The discovery of complex geospatial features, however, is still rather understudied. A complex feature, such as a Weapon of Mass Destruction (WMD) proliferation facility, is spatially composed of elementary features (e.g., buildings for hosting fuel concentration machines, cooling towers, transportation roads, and fences). Such spatial semantics, together with thematic semantics of feature types, can be used to discover complex geospatial features. This paper proposes a workflow-based approach for discovery of complex geospatial features that uses geospatial semantics and services. The elementary features extracted from imagery are archived in distributed Web Feature Services (WFSs) and discoverable from a catalogue service. Using spatial semantics among elementary features and thematic semantics among feature types, workflow-based service chains can be constructed to locate semantically-related complex features in imagery. The workflows are reusable and can provide on-demand discovery of complex features in a distributed environment.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

How to switch on and switch off semantic priming effects for natural and artifactual categories: activation processes in category memory depend on focusing specific feature dimensions.

PubMed

Bermeitinger, Christina; Wentura, Dirk; Frings, Christian

2011-06-01

"Semantic priming" refers to the phenomenon that people react faster to target words preceded by semantically related rather than semantically unrelated words. We wondered whether momentary mind sets modulate semantic priming for natural versus artifactual categories. We interspersed a category priming task with a second task that required participants to react to either the perceptual or action features of simple geometric shapes. Focusing on perceptual features enhanced semantic priming effects for natural categories, whereas focusing on action features enhanced semantic priming effects for artifactual categories. In fact, significant priming effects emerged only for those categories thought to rely on the features activated by the second task. This result suggests that (a) priming effects depend on momentary mind set and (b) features can be weighted flexibly in concept representations; it is also further evidence for sensory-functional accounts of concept and category representation.

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

NASA Astrophysics Data System (ADS)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

The construction of meaning.

PubMed

Kintsch, Walter; Mangalath, Praful

2011-04-01

We argue that word meanings are not stored in a mental lexicon but are generated in the context of working memory from long-term memory traces that record our experience with words. Current statistical models of semantics, such as latent semantic analysis and the Topic model, describe what is stored in long-term memory. The CI-2 model describes how this information is used to construct sentence meanings. This model is a dual-memory model, in that it distinguishes between a gist level and an explicit level. It also incorporates syntactic information about how words are used, derived from dependency grammar. The construction of meaning is conceptualized as feature sampling from the explicit memory traces, with the constraint that the sampling must be contextually relevant both semantically and syntactically. Semantic relevance is achieved by sampling topically relevant features; local syntactic constraints as expressed by dependency relations ensure syntactic relevance. Copyright © 2010 Cognitive Science Society, Inc.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rios Velazquez, E; Parmar, C; Narayan, V

Purpose: To compare the complementary value of quantitative radiomic features to that of radiologist-annotated semantic features in predicting EGFR mutations in lung adenocarcinomas. Methods: Pre-operative CT images of 258 lung adenocarcinoma patients were available. Tumors were segmented using the sing-click ensemble segmentation algorithm. A set of radiomic features was extracted using 3D-Slicer. Test-retest reproducibility and unsupervised dimensionality reduction were applied to select a subset of reproducible and independent radiomic features. Twenty semantic annotations were scored by an expert radiologist, describing the tumor, surrounding tissue and associated findings. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative radiomic and semantic featuresmore » in 172 patients (training-set, temporal split). Radiomic, semantic and combined radiomic-semantic logistic regression models to predict EGFR mutations were evaluated in and independent validation dataset of 86 patients using the area under the receiver operating curve (AUC). Results: EGFR mutations were found in 77/172 (45%) and 39/86 (45%) of the training and validation sets, respectively. Univariate AUCs showed a similar range for both feature types: radiomics median AUC = 0.57 (range: 0.50 – 0.62); semantic median AUC = 0.53 (range: 0.50 – 0.64, Wilcoxon p = 0.55). After MRMR feature selection, the best-performing radiomic, semantic, and radiomic-semantic logistic regression models, for EGFR mutations, showed a validation AUC of 0.56 (p = 0.29), 0.63 (p = 0.063) and 0.67 (p = 0.004), respectively. Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative radiologist annotations. The prognostic value of informative qualitative semantic features such as cavitation and lobulation is increased with the addition of quantitative textural features from the tumor region.« less

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

PubMed

Alor-Hernández, Giner; Pérez-Gallardo, Yuliana; Posada-Gómez, Rubén; Cortes-Robles, Guillermo; Rodríguez-González, Alejandro; Aguilar-Laserre, Alberto A

2012-09-01

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

Language production is facilitated by semantic richness but inhibited by semantic density: Evidence from picture naming.

PubMed

Rabovsky, Milena; Schad, Daniel J; Abdel Rahman, Rasha

2016-01-01

Communicating meaningful messages is the ultimate goal of language production. Yet, verbal messages can differ widely in the complexity and richness of their semantic content, and such differences should strongly modulate conceptual and lexical encoding processes during speech planning. However, despite the crucial role of semantic content in language production, the influence of this variability is currently unclear. Here, we investigate influences of the number of associated semantic features and intercorrelational feature density on language production during picture naming. While the number of semantic features facilitated naming, intercorrelational feature density inhibited naming. Both effects follow naturally from the assumption of conceptual facilitation and simultaneous lexical competition. They are difficult to accommodate with language production theories dismissing lexical competition. Copyright © 2015 Elsevier B.V. All rights reserved.

The Hierarchical Cortical Organization of Human Speech Processing

PubMed Central

de Heer, Wendy A.; Huth, Alexander G.; Griffiths, Thomas L.

2017-01-01

Speech comprehension requires that the brain extract semantic meaning from the spectral features represented at the cochlea. To investigate this process, we performed an fMRI experiment in which five men and two women passively listened to several hours of natural narrative speech. We then used voxelwise modeling to predict BOLD responses based on three different feature spaces that represent the spectral, articulatory, and semantic properties of speech. The amount of variance explained by each feature space was then assessed using a separate validation dataset. Because some responses might be explained equally well by more than one feature space, we used a variance partitioning analysis to determine the fraction of the variance that was uniquely explained by each feature space. Consistent with previous studies, we found that speech comprehension involves hierarchical representations starting in primary auditory areas and moving laterally on the temporal lobe: spectral features are found in the core of A1, mixtures of spectral and articulatory in STG, mixtures of articulatory and semantic in STS, and semantic in STS and beyond. Our data also show that both hemispheres are equally and actively involved in speech perception and interpretation. Further, responses as early in the auditory hierarchy as in STS are more correlated with semantic than spectral representations. These results illustrate the importance of using natural speech in neurolinguistic research. Our methodology also provides an efficient way to simultaneously test multiple specific hypotheses about the representations of speech without using block designs and segmented or synthetic speech. SIGNIFICANCE STATEMENT To investigate the processing steps performed by the human brain to transform natural speech sound into meaningful language, we used models based on a hierarchical set of speech features to predict BOLD responses of individual voxels recorded in an fMRI experiment while subjects listened to natural speech. Both cerebral hemispheres were actively involved in speech processing in large and equal amounts. Also, the transformation from spectral features to semantic elements occurs early in the cortical speech-processing stream. Our experimental and analytical approaches are important alternatives and complements to standard approaches that use segmented speech and block designs, which report more laterality in speech processing and associated semantic processing to higher levels of cortex than reported here. PMID:28588065

Functional imaging of the semantic system: retrieval of sensory-experienced and verbally learned knowledge.

PubMed

Noppeney, Uta; Price, Cathy J

2003-01-01

This paper considers how functional neuro-imaging can be used to investigate the organization of the semantic system and the limitations associated with this technique. The majority of the functional imaging studies of the semantic system have looked for divisions by varying stimulus category. These studies have led to divergent results and no clear anatomical hypotheses have emerged to account for the dissociations seen in behavioral studies. Only a few functional imaging studies have used task as a variable to differentiate the neural correlates of semantic features more directly. We extend these findings by presenting a new study that contrasts tasks that differentially weight sensory (color and taste) and verbally learned (origin) semantic features. Irrespective of the type of semantic feature retrieved, a common semantic system was activated as demonstrated in many previous studies. In addition, the retrieval of verbally learned, but not sensory-experienced, features enhanced activation in medial and lateral posterior parietal areas. We attribute these "verbally learned" effects to differences in retrieval strategy and conclude that evidence for segregation of semantic features at an anatomical level remains weak. We believe that functional imaging has the potential to increase our understanding of the neuronal infrastructure that sustains semantic processing but progress may require multiple experiments until a consistent explanatory framework emerges.

Semantic Shot Classification in Sports Video

NASA Astrophysics Data System (ADS)

Duan, Ling-Yu; Xu, Min; Tian, Qi

2003-01-01

In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of a specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervised learning and classification of the given sports video with low-level and middle-level features extracted from the sports video. It is observed that for each sport we can predefine a small number of semantic shot classes, about 5~10, which covers 90~95% of sports broadcasting video. With the supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot classes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 4 types of sports videos: tennis, basketball, volleyball and soccer. Good classification accuracy of 85~95% has been achieved. With correctly classified sports video shots, further structural and temporal analysis, such as event detection, video skimming, table of content, etc, will be greatly facilitated.

Model-based document categorization employing semantic pattern analysis and local structure clustering

NASA Astrophysics Data System (ADS)

Fume, Kosei; Ishitani, Yasuto

2008-01-01

We propose a document categorization method based on a document model that can be defined externally for each task and that categorizes Web content or business documents into a target category in accordance with the similarity of the model. The main feature of the proposed method consists of two aspects of semantics extraction from an input document. The semantics of terms are extracted by the semantic pattern analysis and implicit meanings of document substructure are specified by a bottom-up text clustering technique focusing on the similarity of text line attributes. We have constructed a system based on the proposed method for trial purposes. The experimental results show that the system achieves more than 80% classification accuracy in categorizing Web content and business documents into 15 or 70 categories.

Semantic Richness and Aging: The Effect of Number of Features in the Lexical Decision Task

ERIC Educational Resources Information Center

Robert, Christelle; Rico Duarte, Liliana

2016-01-01

The aim of this study was to examine whether the effect of semantic richness in visual word recognition (i.e., words with a rich semantic representation are faster to recognize than words with a poorer semantic representation), is changed with aging. Semantic richness was investigated by manipulating the number of features of words (NOF), i.e.,…

Role of Importance and Distinctiveness of Semantic Features in People with Aphasia: A Replication Study

ERIC Educational Resources Information Center

Mason-Baughman, Mary Beth; Wallace, Sarah E.

2014-01-01

Previous studies suggest that people with aphasia have incomplete lexical-semantic representations with decreased low-importance distinctive (LID) feature knowledge. In addition, decreased LID feature knowledge correlates with ability to discriminate among semantically related words. The current study seeks to replicate and extend previous…

Attractor Dynamics and Semantic Neighborhood Density: Processing Is Slowed by Near Neighbors and Speeded by Distant Neighbors

PubMed Central

Mirman, Daniel; Magnuson, James S.

2008-01-01

The authors investigated semantic neighborhood density effects on visual word processing to examine the dynamics of activation and competition among semantic representations. Experiment 1 validated feature-based semantic representations as a basis for computing semantic neighborhood density and suggested that near and distant neighbors have opposite effects on word processing. Experiment 2 confirmed these results: Word processing was slower for dense near neighborhoods and faster for dense distant neighborhoods. Analysis of a computational model showed that attractor dynamics can produce this pattern of neighborhood effects. The authors argue for reconsideration of traditional models of neighborhood effects in terms of attractor dynamics, which allow both inhibitory and facilitative effects to emerge. PMID:18194055

Spatial Relation Predicates in Topographic Feature Semantics

USGS Publications Warehouse

Varanka, Dalia E.; Caro, Holly K.

2013-01-01

Topographic data are designed and widely used for base maps of diverse applications, yet the power of these information sources largely relies on the interpretive skills of map readers and relational database expert users once the data are in map or geographic information system (GIS) form. Advances in geospatial semantic technology offer data model alternatives for explicating concepts and articulating complex data queries and statements. To understand and enrich the vocabulary of topographic feature properties for semantic technology, English language spatial relation predicates were analyzed in three standard topographic feature glossaries. The analytical approach drew from disciplinary concepts in geography, linguistics, and information science. Five major classes of spatial relation predicates were identified from the analysis; representations for most of these are not widely available. The classes are: part-whole (which are commonly modeled throughout semantic and linked-data networks), geometric, processes, human intention, and spatial prepositions. These are commonly found in the ‘real world’ and support the environmental science basis for digital topographical mapping. The spatial relation concepts are based on sets of relation terms presented in this chapter, though these lists are not prescriptive or exhaustive. The results of this study make explicit the concepts forming a broad set of spatial relation expressions, which in turn form the basis for expanding the range of possible queries for topographical data analysis and mapping.

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

NASA Astrophysics Data System (ADS)

Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément

2015-07-01

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Feature relevance assessment for the semantic interpretation of 3D point cloud data

NASA Astrophysics Data System (ADS)

Weinmann, M.; Jutzi, B.; Mallet, C.

2013-10-01

The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

Semantic Context Detection Using Audio Event Fusion

NASA Astrophysics Data System (ADS)

Chu, Wei-Ta; Cheng, Wen-Huang; Wu, Ja-Ling

2006-12-01

Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model) and discriminative (support vector machine (SVM)) approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.

The Fruitless Effort of Growing a Fruitless Tree: Early Morpho-Orthographic and Morpho-Semantic Effects in Sentence Reading

ERIC Educational Resources Information Center

Amenta, Simona; Marelli, Marco; Crepaldi, Davide

2015-01-01

In this eye-tracking study, we investigated how semantics inform morphological analysis at the early stages of visual word identification in sentence reading. We exploited a feature of several derived Italian words, that is, that they can be read in a "morphologically transparent" way or in a "morphologically opaque" way…

Landscape features, standards, and semantics in U.S. national topographic mapping databases

USGS Publications Warehouse

Varanka, Dalia

2009-01-01

The objective of this paper is to examine the contrast between local, field-surveyed topographical representation and feature representation in digital, centralized databases and to clarify their ontological implications. The semantics of these two approaches are contrasted by examining the categorization of features by subject domains inherent to national topographic mapping. When comparing five USGS topographic mapping domain and feature lists, results indicate that multiple semantic meanings and ontology rules were applied to the initial digital database, but were lost as databases became more centralized at national scales, and common semantics were replaced by technological terms.

Is attention enough? A re-examination of the impact of feature-specific attention allocation on semantic priming effects in the pronunciation task.

PubMed

Becker, Manuel; Klauer, Karl Christoph; Spruyt, Adriaan

2016-02-01

In a series of articles, Spruyt and colleagues have developed the Feature-Specific Attention Allocation framework, stating that the semantic analysis of task-irrelevant stimuli is critically dependent upon dimension-specific attention allocation. In an adversarial collaboration, we replicate one experiment supporting this theory (Spruyt, de Houwer, & Hermans, 2009; Exp. 3), in which semantic priming effects in the pronunciation task were found to be restricted to stimulus dimensions that were task-relevant on induction trials. Two pilot studies showed the capability of our laboratory to detect priming effects in the pronunciation task, but also suggested that the original effect may be difficult to replicate. In this study, we tried to replicate the original experiment while ensuring adequate statistical power. Results show little evidence for dimension-specific priming effects. The present results provide further insight into the malleability of early semantic encoding processes, but also show the need for further research on this topic.

The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects

PubMed Central

Laszlo, Sarah; Federmeier, Kara D.

2010-01-01

Linking print with meaning tends to be divided into subprocesses, such as recognition of an input's lexical entry and subsequent access of semantics. However, recent results suggest that the set of semantic features activated by an input is broader than implied by a view wherein access serially follows recognition. EEG was collected from participants who viewed items varying in number and frequency of both orthographic neighbors and lexical associates. Regression analysis of single item ERPs replicated past findings, showing that N400 amplitudes are greater for items with more neighbors, and further revealed that N400 amplitudes increase for items with more lexical associates and with higher frequency neighbors or associates. Together, the data suggest that in the N400 time window semantic features of items broadly related to inputs are active, consistent with models in which semantic access takes place in parallel with stimulus recognition. PMID:20624252

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

Geospatial-temporal semantic graph representations of trajectories from remote sensing and geolocation data

DOEpatents

Perkins, David Nikolaus; Brost, Randolph; Ray, Lawrence P.

2017-08-08

Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasets relative to moving objects such as vehicles.

Unconscious semantic activation depends on feature-specific attention allocation.

PubMed

Spruyt, Adriaan; De Houwer, Jan; Everaert, Tom; Hermans, Dirk

2012-01-01

We examined whether semantic activation by subliminally presented stimuli is dependent upon the extent to which participants assign attention to specific semantic stimulus features and stimulus dimensions. Participants pronounced visible target words that were preceded by briefly presented, masked prime words. Both affective and non-affective semantic congruence of the prime-target pairs were manipulated under conditions that either promoted selective attention for affective stimulus information or selective attention for non-affective semantic stimulus information. In line with our predictions, results showed that affective congruence had a clear impact on word pronunciation latencies only if participants were encouraged to assign attention to the affective stimulus dimension. In contrast, non-affective semantic relatedness of the prime-target pairs produced no priming at all. Our findings are consistent with the hypothesis that unconscious activation of (affective) semantic information is modulated by feature-specific attention allocation. Copyright © 2011 Elsevier B.V. All rights reserved.

Visual affective classification by combining visual and text features.

PubMed

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task.

Visual affective classification by combining visual and text features

PubMed Central

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task. PMID:28850566

On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

NASA Astrophysics Data System (ADS)

Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

Principal semantic components of language and the measurement of meaning.

PubMed

Samsonovich, Alexei V; Samsonovic, Alexei V; Ascoli, Giorgio A

2010-06-11

Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of "good/bad" (valence), "calm/excited" (arousal), and "open/closed" (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number ( approximately 4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a universal metric system for semantics of human experiences, which is necessary for developing a rigorous science of the mind.

Semantic feature analysis treatment for anomia in two fluent aphasia syndromes.

PubMed

Boyle, Mary

2004-08-01

The effect of semantic feature analysis (SFA) treatment on confrontation naming and discourse production was examined in 2 persons, 1 with anomic aphasia and 1 with Wernicke's aphasia. Results indicated that confrontation naming of treated nouns improved and generalized to untreated nouns for both participants, who appeared to have different lexical access impairments. Both participants demonstrated improvement in some aspects of discourse production associated with the confrontation naming SFA treatment. However, there was no change in most manifestations of lexical retrieval difficulty during discourse for either participant. These findings support previous work regarding improved and generalized naming associated with SFA treatment and indicate a need to examine effects of improved confrontation naming on more natural speaking situations.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

NASA Astrophysics Data System (ADS)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Semantic Feature Distinctiveness and Frequency

ERIC Educational Resources Information Center

Lamb, Katherine M.

2012-01-01

Lexical access is the process in which basic components of meaning in language, the lexical entries (words) are activated. This activation is based on the organization and representational structure of the lexical entries. Semantic features of words, which are the prominent semantic characteristics of a word concept, provide important information…

Unconscious Semantic Activation Depends on Feature-Specific Attention Allocation

ERIC Educational Resources Information Center

Spruyt, Adriaan; De Houwer, Jan; Everaert, Tom; Hermans, Dirk

2012-01-01

We examined whether semantic activation by subliminally presented stimuli is dependent upon the extent to which participants assign attention to specific semantic stimulus features and stimulus dimensions. Participants pronounced visible target words that were preceded by briefly presented, masked prime words. Both affective and non-affective…

Qualitative dynamics semantics for SBGN process description.

PubMed

Rougny, Adrien; Froidevaux, Christine; Calzone, Laurence; Paulevé, Loïc

2016-06-16

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far. We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F. The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

Discovering semantic features in the literature: a foundation for building functional associations

PubMed Central

Chagoyen, Monica; Carmona-Saez, Pedro; Shatkay, Hagit; Carazo, Jose M; Pascual-Montano, Alberto

2006-01-01

Background Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. Results We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. Conclusion The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data. PMID:16438716

The use of a modified semantic features analysis approach in aphasia.

PubMed

Hashimoto, Naomi; Frome, Amber

2011-01-01

Several studies have reported improved naming using the semantic feature analysis (SFA) approach in individuals with aphasia. Whether the SFA can be modified and still produce naming improvements in aphasia is unknown. The present study was designed to address this question by using a modified version of the SFA approach. Three, rather than the typical six, features were used, and written along with verbal responses were allowed in an individual with both aphasia and apraxia of speech. A single-subject multiple-baseline design across behaviors was used to treat naming of single objects across three different semantic categories in a 72-year-old individual with aphasia and apraxia of speech. Stimulus generalization of training was measured by using photographs of trained items presented in natural contexts. Training of the three different categories resulted in improved naming. At a 6-week follow-up session, naming remained above pre-treatment levels but declines were noted compared to treatment levels. Generalization to the same trained items presented in different contexts was also demonstrated although declines in performance were also noted over time. Results of the study provide qualified support for the use of three features in promoting long-term improvement of naming in an individual with both aphasia and apraxia of speech. Future SFA studies should focus on whether it is the number or types of features used, aphasia severity, or length of treatment that are critical factors in rehabilitating naming deficits in aphasia. Copyright © 2011 Elsevier Inc. All rights reserved.

Impairments in the Face-Processing Network in Developmental Prosopagnosia and Semantic Dementia

PubMed Central

Mendez, Mario F.; Ringman, John M.; Shapira, Jill S.

2015-01-01

Background Developmental prosopagnosia (DP) and semantic dementia (SD) may be the two most common neurologic disorders of face processing, but their main clinical and pathophysiologic differences have not been established. To identify those features, we compared patients with DP and SD. Methods Five patients with DP, five with right temporal-predominant SD, and ten normal controls underwent cognitive, visual perceptual, and face-processing tasks. Results Although the patients with SD were more cognitively impaired than those with DP, the two groups did not differ statistically on the visual perceptual tests. On the face-processing tasks, the DP group had difficulty with configural analysis and they reported relying on serial, feature-by-feature analysis or awareness of salient features to recognize faces. By contrast, the SD group had problems with person knowledge and made semantically related errors. The SD group had better face familiarity scores, suggesting a potentially useful clinical test for distinguishing SD from DP. Conclusions These two disorders of face processing represent clinically distinguishable disturbances along a right hemisphere face-processing network: DP, characterized by early configural agnosia for faces, and SD, characterized primarily by a multimodal person knowledge disorder. We discuss these preliminary findings in the context of the current literature on the face-processing network; recent studies suggest an additional right anterior temporal, unimodal face familiarity-memory deficit consistent with an “associative prosopagnosia.” PMID:26705265

SALT: The Simulator for the Analysis of LWP Timing

NASA Technical Reports Server (NTRS)

Springer, Paul L.; Rodrigues, Arun; Brockman, Jay

2006-01-01

With the emergence of new processor architectures that are highly multithreaded, and support features such as full/empty memory semantics and split-phase memory transactions, the need for a processor simulator to handle these features becomes apparent. This paper describes such a simulator, called SALT.

LDRD final report :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brost, Randolph C.; McLendon, William Clarence,

2013-01-01

Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report amore » preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.« less

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval

PubMed Central

Bonnici, Heidi M.; Richter, Franziska R.; Yazar, Yasemin

2016-01-01

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. SIGNIFICANCE STATEMENT Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. PMID:27194327

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval.

PubMed

Bonnici, Heidi M; Richter, Franziska R; Yazar, Yasemin; Simons, Jon S

2016-05-18

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. Copyright © 2016 Bonnici, Richter, et al.

A Collection of Features for Semantic Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eliassi-Rad, T; Fodor, I K; Gallagher, B

2007-05-02

Semantic graphs are commonly used to represent data from one or more data sources. Such graphs extend traditional graphs by imposing types on both nodes and links. This type information defines permissible links among specified nodes and can be represented as a graph commonly referred to as an ontology or schema graph. Figure 1 depicts an ontology graph for data from National Association of Securities Dealers. Each node type and link type may also have a list of attributes. To capture the increased complexity of semantic graphs, concepts derived for standard graphs have to be extended. This document explains brieflymore » features commonly used to characterize graphs, and their extensions to semantic graphs. This document is divided into two sections. Section 2 contains the feature descriptions for static graphs. Section 3 extends the features for semantic graphs that vary over time.« less

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

Comparison between semantic features and lung-RADS in predicting malignancy of screening lung nodule

PubMed Central

Li, Qian; Balagurunathan, Yoganand; Liu, Ying; Qi, Jin; Schabath, Matthew B.; Ye, Zhaoxiang; Gillies, Robert

2017-01-01

Rationale Lung-RADS is proposed for the Low-dose computed tomography (LDCT) interpretation in lung cancer screening, but its performance needs to be further evaluated. Objectives To compare the value of radiological semantic features and lung-RADS in predicting nodule malignancy risk at different screening rounds, and to investigate whether the predictive power of lung-RADS could be improved by incorporating semantic features. Methods A training cohort of 199 patients (139 benign and 60 cancerous nodules diagnosed at the third screening round), and a testing cohort of 80 patients (40 benign and 40 malignant nodules) were obtained from the National Lung Screening Trial dataset. A multivariate linear predictor model was built based on the 24 systematically scored semantic features, and the performances were compared to lung-RADS (scale 3 or above called positive). Measurements and Main Results Among the semantic features, contour and border definition were the top individual predictors. The average area under the receiver-operating characteristic curve (AUC) of border definition at baseline (T0) was 0.724. The average AUC of contour at first (T1) and second follow-up (T2) were 0.843 and 0.878, respectively. Other significant features included size, location, vessel attachment, solidity, focal emphysema and focal fibrosis. In comparison, the average AUC of lung-RADS at T0, T1 and T2 were 0.600, 0.760 and 0.867, respectively, and could be improved to 0.743, 0.887 and 0.968 by adding semantic features. Conclusion The semantic features performed similar to lung-RADS at follow-ups, outperformed lung-RADS at baseline, and could improve the performance of lung-RADS for all screening rounds. PMID:29137847

Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial

NASA Astrophysics Data System (ADS)

Li, Qian; Balagurunathan, Yoganand; Liu, Ying; Schabath, Matthew; Gillies, Robert J.

2016-03-01

Background: Lung-RADS is the new oncology classification guideline proposed by American College of Radiology (ACR), which provides recommendation for further follow up in lung cancer screening. However, only two features (solidity and size) are included in this system. We hypothesize that additional sematic features can be used to better characterize lung nodules and diagnose cancer. Objective: We propose to develop and characterize a systematic methodology based on semantic image traits to more accurately predict occurrence of cancerous nodules. Methods: 24 radiological image traits were systematically scored on a point scale (up to 5) by a trained radiologist, and lung-RADS was independently scored. A linear discriminant model was used on the semantic features to access their performance in predicting cancer status. The semantic predictors were then compared to lung-RADS classification in 199 patients (60 cancers, 139 normal controls) obtained from the National Lung Screening Trial. Result: There were different combinations of semantic features that were strong predictors of cancer status. Of these, contour, border definition, size, solidity, focal emphysema, focal fibrosis and location emerged as top candidates. The performance of two semantic features (short axial diameter and contour) had an AUC of 0.945, and was comparable to that of lung-RADS (AUC: 0.871). Conclusion: We propose that a semantics-based discrimination approach may act as a complement to the lung-RADS to predict cancer status.

Abnormal semantic knowledge in a case of developmental amnesia.

PubMed

Blumenthal, Anna; Duke, Devin; Bowles, Ben; Gilboa, Asaf; Rosenbaum, R Shayna; Köhler, Stefan; McRae, Ken

2017-07-28

An important theory holds that semantic knowledge can develop independently of episodic memory. One strong source of evidence supporting this independence comes from the observation that individuals with early hippocampal damage leading to developmental amnesia generally perform normally on standard tests of semantic memory, despite their profound impairment in episodic memory. However, one aspect of semantic memory that has not been explored is conceptual structure. We built on the theoretically important distinction between intrinsic features of object concepts (e.g., shape, colour, parts) and extrinsic features (e.g., how something is used, where it is typically located). The accrual of extrinsic feature knowledge that is important for concepts such as chair or spoon may depend on binding mechanisms in the hippocampus. We tested HC, an individual with developmental amnesia due to a well-characterized lesion of the hippocampus, on her ability to generate semantic features for object concepts. HC generated fewer extrinsic features than controls, but a similar number of intrinsic features than controls. We also tested her on typicality ratings. Her typicality ratings were abnormal for nonliving things (which more strongly depend on extrinsic features), but normal for living things (which more strongly depend on intrinsic features). In contrast, NB, who has MTL but not hippocampal damage due to surgery, showed no impairments in either task. These results suggest that episodic and semantic memory are not entirely independent, and that the hippocampus is important for learning some aspects of conceptual knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Formal Semantics of PVS

NASA Technical Reports Server (NTRS)

Owre, Sam; Shankar, Natarajan

1999-01-01

A specification language is a medium for expressing what is computed rather than how it is computed. Specification languages share some features with programming languages but are also different in several important ways. For our purpose, a specification language is a logic within which the behavior of computational systems can be formalized. Although a specification can be used to simulate the behavior of such systems, we mainly use specifications to state and prove system properties with mechanical assistance. We present the formal semantics of the specification language of SRI's Prototype Verification System (PVS). This specification language is based on the simply typed lambda calculus. The novelty in PVS is that it contains very expressive language features whose static analysis (e.g., typechecking) requires the assistance of a theorem prover. The formal semantics illuminates several of the design considerations underlying PVS, the interaction between theorem proving and typechecking.

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes.

PubMed

Sadeghi, Zahra; McClelland, James L; Hoffman, Paul

2015-09-01

An influential position in lexical semantics holds that semantic representations for words can be derived through analysis of patterns of lexical co-occurrence in large language corpora. Firth (1957) famously summarised this principle as "you shall know a word by the company it keeps". We explored whether the same principle could be applied to non-verbal patterns of object co-occurrence in natural scenes. We performed latent semantic analysis (LSA) on a set of photographed scenes in which all of the objects present had been manually labelled. This resulted in a representation of objects in a high-dimensional space in which similarity between two objects indicated the degree to which they appeared in similar scenes. These representations revealed similarities among objects belonging to the same taxonomic category (e.g., items of clothing) as well as cross-category associations (e.g., between fruits and kitchen utensils). We also compared representations generated from this scene dataset with two established methods for elucidating semantic representations: (a) a published database of semantic features generated verbally by participants and (b) LSA applied to a linguistic corpus in the usual fashion. Statistical comparisons of the three methods indicated significant association between the structures revealed by each method, with the scene dataset displaying greater convergence with feature-based representations than did LSA applied to linguistic data. The results indicate that information about the conceptual significance of objects can be extracted from their patterns of co-occurrence in natural environments, opening the possibility for such data to be incorporated into existing models of conceptual representation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

PubMed Central

Valavanis, Ioannis; Pilalis, Eleftherios; Georgiadis, Panagiotis; Kyrtopoulos, Soterios; Chatziioannou, Aristotelis

2015-01-01

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies. PMID:27600245

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

Recognizable or Not: Towards Image Semantic Quality Assessment for Compression

NASA Astrophysics Data System (ADS)

Liu, Dong; Wang, Dandan; Li, Houqiang

2017-12-01

Traditionally, image compression was optimized for the pixel-wise fidelity or the perceptual quality of the compressed images given a bit-rate budget. But recently, compressed images are more and more utilized for automatic semantic analysis tasks such as recognition and retrieval. For these tasks, we argue that the optimization target of compression is no longer perceptual quality, but the utility of the compressed images in the given automatic semantic analysis task. Accordingly, we propose to evaluate the quality of the compressed images neither at pixel level nor at perceptual level, but at semantic level. In this paper, we make preliminary efforts towards image semantic quality assessment (ISQA), focusing on the task of optical character recognition (OCR) from compressed images. We propose a full-reference ISQA measure by comparing the features extracted from text regions of original and compressed images. We then propose to integrate the ISQA measure into an image compression scheme. Experimental results show that our proposed ISQA measure is much better than PSNR and SSIM in evaluating the semantic quality of compressed images; accordingly, adopting our ISQA measure to optimize compression for OCR leads to significant bit-rate saving compared to using PSNR or SSIM. Moreover, we perform subjective test about text recognition from compressed images, and observe that our ISQA measure has high consistency with subjective recognizability. Our work explores new dimensions in image quality assessment, and demonstrates promising direction to achieve higher compression ratio for specific semantic analysis tasks.

Salient object detection method based on multiple semantic features

NASA Astrophysics Data System (ADS)

Wang, Chunyang; Yu, Chunyan; Song, Meiping; Wang, Yulei

2018-04-01

The existing salient object detection model can only detect the approximate location of salient object, or highlight the background, to resolve the above problem, a salient object detection method was proposed based on image semantic features. First of all, three novel salient features were presented in this paper, including object edge density feature (EF), object semantic feature based on the convex hull (CF) and object lightness contrast feature (LF). Secondly, the multiple salient features were trained with random detection windows. Thirdly, Naive Bayesian model was used for combine these features for salient detection. The results on public datasets showed that our method performed well, the location of salient object can be fixed and the salient object can be accurately detected and marked by the specific window.

The role of semantic complexity in treatment of naming deficits: training semantic categories in fluent aphasia by controlling exemplar typicality.

PubMed

Kiran, Swathi; Thompson, Cynthia K

2003-06-01

The effect of typicality of category exemplars on naming was investigated using a single subject experimental design across participants and behaviors in 4 patients with fluent aphasia. Participants received a semantic feature treatment to improve naming of either typical or atypical items within semantic categories, while generalization was tested to untrained items of the category. The order of typicality and category trained was counterbalanced across participants. Results indicated that patients trained on naming of atypical exemplars demonstrated generalization to naming of intermediate and typical items. However, patients trained on typical items demonstrated no generalized naming effect to intermediate or atypical examples. Furthermore, analysis of errors indicated an evolution of errors throughout training, from those with no apparent relationship to the target to primarily semantic and phonemic paraphasias. Performance on standardized language tests also showed changes as a function of treatment. Theoretical and clinical implications regarding the impact of considering semantic complexity on rehabilitation of naming deficits in aphasia are discussed.

The role of semantic complexity in treatment of naming deficits: training semantic categories in fluent aphasia by controlling exemplar typicality.

PubMed

Kiran, Swathi; Thompson, Cynthia K

2003-08-01

The effect of typicality of category exemplars on naming was investigated using a single subject experimental design across participants and behaviors in 4 patients with fluent aphasia. Participants received a semantic feature treatment to improve naming of either typical or atypical items within semantic categories, while generalization was tested to untrained items of the category. The order of typicality and category trained was counterbalanced across participants. Results indicated that patients trained on naming of atypical exemplars demonstrated generalization to naming of intermediate and typical items. However, patients trained on typical items demonstrated no generalized naming effect to intermediate or atypical examples. Furthermore, analysis of errors indicated an evolution of errors throughout training, from those with no apparent relationship to the target to primarily semantic and phonemic paraphasias. Performance on standardized language tests also showed changes as a function of treatment. Theoretical and clinical implications regarding the impact of considering semantic complexity on rehabilitation of naming deficits in aphasia are discussed.

Refractory Access Disorders and the Organization of Concrete and Abstract Semantics: Do they Differ?

PubMed Central

Hamilton, A. Cris; Coslett, H. Branch

2010-01-01

Patients with “refractory semantic access deficits” demonstrate several unique features that make them important sources of insight into the organization of semantic representations. Here we attempt to replicate several novel findings from single-case studies reported in the literature. Patient UM– 103 displays the cardinal features of a “refractory semantic access deficit” and showed many of the same effects of semantic relatedness reported in the literature. However, when probing concrete and abstract words, this patient revealed very different patterns of performance compared to two previously reported patients. We discuss the implications of our data for models of semantic organization of abstract and concrete words. PMID:18569737

Semantically Induced Distortions of Visual Awareness in a Patient with Balint's Syndrome

ERIC Educational Resources Information Center

Soto, David; Humphreys, Glyn W.

2009-01-01

We present data indicating that visual awareness for a basic perceptual feature (colour) can be influenced by the relation between the feature and the semantic properties of the stimulus. We examined semantic interference from the meaning of a colour word ("RED") on simple colour (ink related) detection responses in a patient with simultagnosia…

User-oriented summary extraction for soccer video based on multimodal analysis

NASA Astrophysics Data System (ADS)

Liu, Huayong; Jiang, Shanshan; He, Tingting

2011-11-01

An advanced user-oriented summary extraction method for soccer video is proposed in this work. Firstly, an algorithm of user-oriented summary extraction for soccer video is introduced. A novel approach that integrates multimodal analysis, such as extraction and analysis of the stadium features, moving object features, audio features and text features is introduced. By these features the semantic of the soccer video and the highlight mode are obtained. Then we can find the highlight position and put them together by highlight degrees to obtain the video summary. The experimental results for sports video of world cup soccer games indicate that multimodal analysis is effective for soccer video browsing and retrieval.

Spanish semantic feature production norms for 400 concrete concepts.

PubMed

Vivas, Jorge; Vivas, Leticia; Comesaña, Ana; Coni, Ana García; Vorano, Agostina

2017-06-01

Semantic feature production norms provide many quantitative measures of different feature and concept variables that are necessary to solve some debates surrounding the nature of the organization, both normal and pathological, of semantic memory. Despite the current existence of norms for different languages, there are still no published norms in Spanish. This article presents a new set of norms collected from 810 participants for 400 living and nonliving concepts among Spanish speakers. These norms consist of empirical collections of features that participants used to describe the concepts. Four files were elaborated: a concept-feature file, a concept-concept matrix, a feature-feature matrix, and a significantly correlated features file. We expect that these norms will be useful for researchers in the fields of experimental psychology, neuropsychology, and psycholinguistics.

Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing.

PubMed

Fan, Jianping; Luo, Hangzai; Elmagarmid, Ahmed K

2004-07-01

Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments

PubMed Central

Pereira, Francisco; Botvinick, Matthew; Detre, Greg

2012-01-01

In this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that those features can outperform those in [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects. PMID:23243317

Before the N400: effects of lexical-semantic violations in visual cortex.

PubMed

Dikker, Suzanne; Pylkkanen, Liina

2011-07-01

There exists an increasing body of research demonstrating that language processing is aided by context-based predictions. Recent findings suggest that the brain generates estimates about the likely physical appearance of upcoming words based on syntactic predictions: words that do not physically look like the expected syntactic category show increased amplitudes in the visual M100 component, the first salient MEG response to visual stimulation. This research asks whether violations of predictions based on lexical-semantic information might similarly generate early visual effects. In a picture-noun matching task, we found early visual effects for words that did not accurately describe the preceding pictures. These results demonstrate that, just like syntactic predictions, lexical-semantic predictions can affect early visual processing around ∼100ms, suggesting that the M100 response is not exclusively tuned to recognizing visual features relevant to syntactic category analysis. Rather, the brain might generate predictions about upcoming visual input whenever it can. However, visual effects of lexical-semantic violations only occurred when a single lexical item could be predicted. We argue that this may be due to the fact that in natural language processing, there is typically no straightforward mapping between lexical-semantic fields (e.g., flowers) and visual or auditory forms (e.g., tulip, rose, magnolia). For syntactic categories, in contrast, certain form features do reliably correlate with category membership. This difference may, in part, explain why certain syntactic effects typically occur much earlier than lexical-semantic effects. Copyright © 2011 Elsevier Inc. All rights reserved.

Attractor Dynamics and Semantic Neighborhood Density: Processing Is Slowed by Near Neighbors and Speeded by Distant Neighbors

ERIC Educational Resources Information Center

Mirman, Daniel; Magnuson, James S.

2008-01-01

The authors investigated semantic neighborhood density effects on visual word processing to examine the dynamics of activation and competition among semantic representations. Experiment 1 validated feature-based semantic representations as a basis for computing semantic neighborhood density and suggested that near and distant neighbors have…

Rhetorical features facilitate prosodic processing while handicapping ease of semantic comprehension.

PubMed

Menninghaus, Winfried; Bohrn, Isabel C; Knoop, Christine A; Kotz, Sonja A; Schlotz, Wolff; Jacobs, Arthur M

2015-10-01

Studies on rhetorical features of language have reported both enhancing and adverse effects on ease of processing. We hypothesized that two explanations may account for these inconclusive findings. First, the respective gains and losses in ease of processing may apply to different dimensions of language processing (specifically, prosodic and semantic processing) and different types of fluency (perceptual vs. conceptual) and may well allow for an integration into a more comprehensive framework. Second, the effects of rhetorical features may be sensitive to interactions with other rhetorical features; employing a feature separately or in combination with others may then predict starkly different effects. We designed a series of experiments in which we expected the same rhetorical features of the very same sentences to exert adverse effects on semantic (conceptual) fluency and enhancing effects on prosodic (perceptual) fluency. We focused on proverbs that each employ three rhetorical features: rhyme, meter, and brevitas (i.e., artful shortness). The presence of these target features decreased ease of conceptual fluency (semantic comprehension) while enhancing perceptual fluency as reflected in beauty and succinctness ratings that were mainly driven by prosodic features. The rhetorical features also predicted choices for persuasive purposes, yet only for the sentence versions featuring all three rhetorical features; the presence of only one or two rhetorical features had an adverse effect on the choices made. We suggest that the facilitating effects of a combination of rhyme, meter, and rhetorical brevitas on perceptual (prosodic) fluency overcompensated for their adverse effects on conceptual (semantic) fluency, thus resulting in a total net gain both in processing ease and in choices for persuasive purposes. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Evidence of semantic processing impairments in behavioural variant frontotemporal dementia and Parkinson's disease.

PubMed

Cousins, Katheryn A Q; Grossman, Murray

2017-12-01

Category-specific impairments caused by brain damage can provide important insights into how semantic concepts are organized in the brain. Recent research has demonstrated that disease to sensory and motor cortices can impair perceptual feature knowledge important to the representation of semantic concepts. This evidence supports the grounded cognition theory of semantics, the view that lexical knowledge is partially grounded in perceptual experience and that sensory and motor regions support semantic representations. Less well understood, however, is how heteromodal semantic hubs work to integrate and process semantic information. Although the majority of semantic research to date has focused on how sensory cortical areas are important for the representation of semantic features, new research explores how semantic memory is affected by neurodegeneration in regions important for semantic processing. Here, we review studies that demonstrate impairments to abstract noun knowledge in behavioural variant frontotemporal degeneration (bvFTD) and to action verb knowledge in Parkinson's disease, and discuss how these deficits relate to disease of the semantic selection network. Findings demonstrate that semantic selection processes are supported by the left inferior frontal gyrus (LIFG) and basal ganglia, and that disease to these regions in bvFTD and Parkinson's disease can lead to categorical impairments for abstract nouns and action verbs, respectively.

An Italian battery for the assessment of semantic memory disorders.

PubMed

Catricalà, Eleonora; Della Rosa, Pasquale A; Ginex, Valeria; Mussetti, Zoe; Plebani, Valentina; Cappa, Stefano F

2013-06-01

We report the construction and standardization of a new comprehensive battery of tests for the assessment of semantic memory disorders. The battery is constructed on a common set of 48 stimuli, belonging to both living and non-living categories, rigidly controlled for several confounding variables, and is based on an empirically derived corpus of semantic features. It includes six tasks, in order to assess semantic memory through different modalities of input and output: two naming tasks, one with colored pictures and the other in response to an oral description, a word-picture matching task, a picture sorting task, a free generation of features task and a sentence verification task. Normative data on 106 Italian subjects pooled across homogenous subgroups for age, sex and education are reported. The new battery allows an in-depth investigation of category-specific disorders and of progressive semantic memory deficits at features level, overcoming some of the limitations of existing tests.

A Diffusive-Particle Theory of Free Recall

PubMed Central

Fumarola, Francesco

2017-01-01

Diffusive models of free recall have been recently introduced in the memory literature, but their potential remains largely unexplored. In this paper, a diffusive model of short-term verbal memory is considered, in which the psychological state of the subject is encoded as the instantaneous position of a particle diffusing over a semantic graph. The model is particularly suitable for studying the dependence of free-recall observables on the semantic properties of the words to be recalled. Besides predicting some well-known experimental features (forward asymmetry, semantic clustering, word-length effect), a novel prediction is obtained on the relationship between the contiguity effect and the syllabic length of words; shorter words, by way of their wider semantic range, are predicted to be characterized by stronger forward contiguity. A fresh analysis of archival free-recall data allows to confirm this prediction. PMID:29085521

Visual and semantic processing of living things and artifacts: an FMRI study.

PubMed

Zannino, Gian Daniele; Buccione, Ivana; Perri, Roberta; Macaluso, Emiliano; Lo Gerfo, Emanuele; Caltagirone, Carlo; Carlesimo, Giovanni A

2010-03-01

We carried out an fMRI study with a twofold purpose: to investigate the relationship between networks dedicated to semantic and visual processing and to address the issue of whether semantic memory is subserved by a unique network or by different subsystems, according to semantic category or feature type. To achieve our goals, we administered a word-picture matching task, with within-category foils, to 15 healthy subjects during scanning. Semantic distance between the target and the foil and semantic domain of the target-foil pairs were varied orthogonally. Our results suggest that an amodal, undifferentiated network for the semantic processing of living things and artifacts is located in the anterolateral aspects of the temporal lobes; in fact, activity in this substrate was driven by semantic distance, not by semantic category. By contrast, activity in ventral occipito-temporal cortex was driven by category, not by semantic distance. We interpret the latter finding as the effect exerted by systematic differences between living things and artifacts at the level of their structural representations and possibly of their lower-level visual features. Finally, we attempt to reconcile contrasting data in the neuropsychological and functional imaging literature on semantic substrate and category specificity.

Altered brain response for semantic knowledge in Alzheimer's disease.

PubMed

Wierenga, Christina E; Stricker, Nikki H; McCauley, Ashley; Simmons, Alan; Jak, Amy J; Chang, Yu-Ling; Nation, Daniel A; Bangen, Katherine J; Salmon, David P; Bondi, Mark W

2011-02-01

Word retrieval deficits are common in Alzheimer's disease (AD) and are thought to reflect a degradation of semantic memory. Yet, the nature of semantic deterioration in AD and the underlying neural correlates of these semantic memory changes remain largely unknown. We examined the semantic memory impairment in AD by investigating the neural correlates of category knowledge (e.g., living vs. nonliving) and featural processing (global vs. local visual information). During event-related fMRI, 10 adults diagnosed with mild AD and 22 cognitively normal (CN) older adults named aloud items from three categories for which processing of specific visual features has previously been dissociated from categorical features. Results showed widespread group differences in the categorical representation of semantic knowledge in several language-related brain areas. For example, the right inferior frontal gyrus showed selective brain response for nonliving items in the CN group but living items in the AD group. Additionally, the AD group showed increased brain response for word retrieval irrespective of category in Broca's homologue in the right hemisphere and rostral cingulate cortex bilaterally, which suggests greater recruitment of frontally mediated neural compensatory mechanisms in the face of semantic alteration. Copyright © 2010 Elsevier Ltd. All rights reserved.

Cross-modal integration of lexical-semantic features during word processing: evidence from oscillatory dynamics during EEG.

PubMed

van Ackeren, Markus J; Rueschemeyer, Shirley-Ann

2014-01-01

In recent years, numerous studies have provided converging evidence that word meaning is partially stored in modality-specific cortical networks. However, little is known about the mechanisms supporting the integration of this distributed semantic content into coherent conceptual representations. In the current study we aimed to address this issue by using EEG to look at the spatial and temporal dynamics of feature integration during word comprehension. Specifically, participants were presented with two modality-specific features (i.e., visual or auditory features such as silver and loud) and asked to verify whether these two features were compatible with a subsequently presented target word (e.g., WHISTLE). Each pair of features described properties from either the same modality (e.g., silver, tiny  =  visual features) or different modalities (e.g., silver, loud  =  visual, auditory). Behavioral and EEG data were collected. The results show that verifying features that are putatively represented in the same modality-specific network is faster than verifying features across modalities. At the neural level, integrating features across modalities induces sustained oscillatory activity around the theta range (4-6 Hz) in left anterior temporal lobe (ATL), a putative hub for integrating distributed semantic content. In addition, enhanced long-range network interactions in the theta range were seen between left ATL and a widespread cortical network. These results suggest that oscillatory dynamics in the theta range could be involved in integrating multimodal semantic content by creating transient functional networks linking distributed modality-specific networks and multimodal semantic hubs such as left ATL.

The representation of semantic knowledge in a child with Williams syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2009-05-01

This study investigated whether there are distinct types of semantic knowledge with distinct representational bases during development. The representation of semantic knowledge in a teenage child (S.T.) with Williams syndrome was explored for the categories of animals, fruit, and vegetables, manipulable objects, and nonmanipulable objects. S.T.'s lexical stores were of a normal size but the volume of "sensory feature" semantic knowledge she generated in oral descriptions was reduced. In visual recognition decisions, S.T. made more false positives to nonitems than did controls. Although overall naming of pictures was unimpaired, S.T. exhibited a category-specific anomia for nonmanipulable objects and impaired naming of visual-feature descriptions of animals. S.T.'s performance was interpreted as reflecting the impaired integration of distinctive features from perceptual input, which may impact upon nonmanipulable objects to a greater extent than the other knowledge categories. Performance was used to inform adult-based models of semantic representation, with category structure proposed to emerge due to differing degrees of dependency upon underlying knowledge types, feature correlations, and the acquisition of information from modality-specific processing modules.

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Semantic similarity between old and new items produces false alarms in recognition memory.

PubMed

Montefinese, Maria; Zannino, Gian Daniele; Ambrosini, Ettore

2015-09-01

In everyday life, human beings can report memories of past events that did not occur or that occurred differently from the way they remember them because memory is an imperfect process of reconstruction and is prone to distortion and errors. In this recognition study using word stimuli, we investigated whether a specific operationalization of semantic similarity among concepts can modulate false memories while controlling for the possible effect of associative strength and word co-occurrence in an old-new recognition task. The semantic similarity value of each new concept was calculated as the mean cosine similarity between pairs of vectors representing that new concept and each old concept belonging to the same semantic category. Results showed that, compared with (new) low-similarity concepts, (new) high-similarity concepts had significantly higher probability of being falsely recognized as old, even after partialling out the effect of confounding variables, including associative relatedness and lexical co-occurrence. This finding supports the feature-based view of semantic memory, suggesting that meaning overlap and sharing of semantic features (which are greater when more similar semantic concepts are being processed) have an influence on recognition performance, resulting in more false alarms for new high-similarity concepts. We propose that the associative strength and word co-occurrence among concepts are not sufficient to explain illusory memories but is important to take into account also the effects of feature-based semantic relations, and, in particular, the semantic similarity among concepts.

Abstract conceptual feature ratings predict gaze within written word arrays: evidence from a Visual Wor(l)d paradigm

PubMed Central

Primativo, Silvia; Reilly, Jamie; Crutch, Sebastian J

2016-01-01

The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eye tracking via an adaptation of the classical ‘visual word paradigm’. Healthy adults (N=20) selected the lexical item most related to a probe word in a 4-item written word array comprising the target and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye-movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were none the less significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe-target similarity at least as well as latent semantic analysis ratings which are based on word co-occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information (not restricted to sensorimotor and emotion information) shape the organization of abstract concepts. Whilst the adapted ‘visual word paradigm’ is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. PMID:26901571

Towards semantically sensitive text clustering: a feature space modeling technology based on dimension extension.

PubMed

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach.

Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension

PubMed Central

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach. PMID:25794172

Image segmentation via foreground and background semantic descriptors

NASA Astrophysics Data System (ADS)

Yuan, Ding; Qiang, Jingjing; Yin, Jihao

2017-09-01

In the field of image processing, it has been a challenging task to obtain a complete foreground that is not uniform in color or texture. Unlike other methods, which segment the image by only using low-level features, we present a segmentation framework, in which high-level visual features, such as semantic information, are used. First, the initial semantic labels were obtained by using the nonparametric method. Then, a subset of the training images, with a similar foreground to the input image, was selected. Consequently, the semantic labels could be further refined according to the subset. Finally, the input image was segmented by integrating the object affinity and refined semantic labels. State-of-the-art performance was achieved in experiments with the challenging MSRC 21 dataset.

Function Follows Form: Activation of Shape and Function Features during Object Identification

ERIC Educational Resources Information Center

Yee, Eiling; Huffstetler, Stacy; Thompson-Schill, Sharon L.

2011-01-01

Most theories of semantic memory characterize knowledge of a given object as comprising a set of semantic features. But how does conceptual activation of these features proceed during object identification? We present the results of a pair of experiments that demonstrate that object recognition is a dynamically unfolding process in which function…

Distinctive Features Hold a Privileged Status in the Computation of Word Meaning: Implications for Theories of Semantic Memory

ERIC Educational Resources Information Center

Cree, George S.; McNorgan, Chris; McRae, Ken

2006-01-01

The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure…

Target-Oriented High-Resolution SAR Image Formation via Semantic Information Guided Regularizations

NASA Astrophysics Data System (ADS)

Hou, Biao; Wen, Zaidao; Jiao, Licheng; Wu, Qian

2018-04-01

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image. However, since the simple prior of low level features are insufficient to describe different semantic contents in the image, this type of regularizer will be incapable of distinguishing between the target of interest and unconcerned background clutters. As a consequence, the features belonging to the target and clutters are simultaneously affected in the generated image without concerning their underlying semantic labels. To address this problem, we propose a novel semantic information guided framework for target oriented SAR image formation, which aims at enhancing the interested target scatters while suppressing the background clutters. Firstly, we develop a new semantics-specific regularizer for image formation by exploiting the statistical properties of different semantic categories in a target scene SAR image. In order to infer the semantic label for each pixel in an unsupervised way, we moreover induce a novel high-level prior-driven regularizer and some semantic causal rules from the prior knowledge. Finally, our regularized framework for image formation is further derived as a simple iteratively reweighted $\\ell_1$ minimization problem which can be conveniently solved by many off-the-shelf solvers. Experimental results demonstrate the effectiveness and superiority of our framework for SAR image formation in terms of target enhancement and clutters suppression, compared with the state of the arts. Additionally, the proposed framework opens a new direction of devoting some machine learning strategies to image formation, which can benefit the subsequent decision making tasks.

Memory Scanning, Introversion-Extraversion, and Levels of Processing.

ERIC Educational Resources Information Center

Eysenck, Michael W.; Eysenck, M. Christine

1979-01-01

Investigated was the hypothesis that high arousal increases processing of physical characteristics and reduces processing of semantic characteristics. While introverts and extroverts had equivalent scanning rates for physical features, introverts were significantly slower in searching for semantic features of category membership, indicating…

An approach to define semantics for BPM systems interoperability

NASA Astrophysics Data System (ADS)

Rico, Mariela; Caliusco, María Laura; Chiotti, Omar; Rosa Galli, María

2015-04-01

This article proposes defining semantics for Business Process Management systems interoperability through the ontology of Electronic Business Documents (EBD) used to interchange the information required to perform cross-organizational processes. The semantic model generated allows aligning enterprise's business processes to support cross-organizational processes by matching the business ontology of each business partner with the EBD ontology. The result is a flexible software architecture that allows dynamically defining cross-organizational business processes by reusing the EBD ontology. For developing the semantic model, a method is presented, which is based on a strategy for discovering entity features whose interpretation depends on the context, and representing them for enriching the ontology. The proposed method complements ontology learning techniques that can not infer semantic features not represented in data sources. In order to improve the representation of these entity features, the method proposes using widely accepted ontologies, for representing time entities and relations, physical quantities, measurement units, official country names, and currencies and funds, among others. When the ontologies reuse is not possible, the method proposes identifying whether that feature is simple or complex, and defines a strategy to be followed. An empirical validation of the approach has been performed through a case study.

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling.

PubMed

Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi

2015-01-01

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

Selective impairment of living things and musical instruments on a verbal 'Semantic Knowledge Questionnaire' in a case of apperceptive visual agnosia.

PubMed

Masullo, Carlo; Piccininni, Chiara; Quaranta, Davide; Vita, Maria Gabriella; Gaudino, Simona; Gainotti, Guido

2012-10-01

Semantic memory was investigated in a patient (MR) affected by a severe apperceptive visual agnosia, due to an ischemic cerebral lesion, bilaterally affecting the infero-mesial parts of the temporo-occipital cortices. The study was made by means of a Semantic Knowledge Questionnaire (Laiacona, Barbarotto, Trivelli, & Capitani, 1993), which takes separately into account four categories of living beings (animals, fruits, vegetables and body parts) and of artefacts (furniture, tools, vehicles and musical instruments), does not require a visual analysis and allows to distinguish errors concerning super-ordinate categorization, perceptual features and functional/encyclopedic knowledge. When the total number of errors obtained on all the categories of living and non-living beings was considered, a non-significant trend toward a higher number of errors in living stimuli was observed. This difference, however, became significant when body parts and musical instruments were excluded from the analysis. Furthermore, the number of errors obtained on the musical instruments was similar to that obtained on the living categories of animals, fruits and vegetables and significantly higher of that obtained in the other artefact categories. This difference was still significant when familiarity, frequency of use and prototypicality of each stimulus entered into a logistic regression analysis. On the other hand, a separate analysis of errors obtained on questions exploring super-ordinate categorization, perceptual features and functional/encyclopedic attributes showed that the differences between living and non-living stimuli and between musical instruments and other artefact categories were mainly due to errors obtained on questions exploring perceptual features. All these data are at variance with the 'domains of knowledge' hypothesis', which assumes that the breakdown of different categories of living and non-living things respects the distinction between biological entities and artefacts and support the models assuming that 'category-specific semantic disorders' are the by-product of the differential weighting that visual-perceptual and functional (or action-related) attributes have in the construction of different biological and artefacts categories. Copyright © 2012 Elsevier Inc. All rights reserved.

Tracking neural coding of perceptual and semantic features of concrete nouns

PubMed Central

Sudre, Gustavo; Pomerleau, Dean; Palatucci, Mark; Wehbe, Leila; Fyshe, Alona; Salmelin, Riitta; Mitchell, Tom

2015-01-01

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. PMID:22565201

Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies

PubMed Central

Köhler, Sebastian; Schulz, Marcel H.; Krawitz, Peter; Bauer, Sebastian; Dölken, Sandra; Ott, Claus E.; Mundlos, Christine; Horn, Denise; Mundlos, Stefan; Robinson, Peter N.

2009-01-01

The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders. PMID:19800049

Neural pattern similarity underlies the mnemonic advantages for living words.

PubMed

Xiao, Xiaoqian; Dong, Qi; Chen, Chuansheng; Xue, Gui

2016-06-01

It has been consistently shown that words representing living things are better remembered than words representing nonliving things, yet the underlying cognitive and neural mechanisms have not been clearly elucidated. The present study used both univariate and multivariate pattern analyses to examine the hypotheses that living words are better remembered because (1) they draw more attention and/or (2) they share more overlapping semantic features. Subjects were asked to study a list of living and nonliving words during a semantic judgment task. An unexpected recognition test was administered 30 min later. We found that subjects recognized significantly more living words than nonliving words. Results supported the overlapping semantic feature hypothesis by showing that (a) semantic ratings showed greater semantic similarity for living words than for nonliving words, (b) there was also significantly greater neural global pattern similarity (nGPS) for living words than for nonliving words in the posterior portion of left parahippocampus (LpPHG), (c) the nGPS in the LpPHG reflected the rated semantic similarity, and also mediated the memory differences between two semantic categories, and (d) greater univariate activation was found for living words than for nonliving words in the left hippocampus (LHIP), which mediated the better memory performance for living words and might reflect greater semantic context binding. In contrast, although living words were processed faster and elicited a stronger activity in the dorsal attention network, these differences did not mediate the animacy effect in memory. Taken together, our results provide strong support to the overlapping semantic features hypothesis, and emphasize the important role of semantic organization in episodic memory encoding. Copyright © 2016 Elsevier Ltd. All rights reserved.

Changes in task-based effective connectivity in language networks following rehabilitation in post-stroke patients with aphasia

PubMed Central

Kiran, Swathi; Meier, Erin L.; Kapse, Kushal J.; Glynn, Peter A.

2015-01-01

In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, LSFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and that this similarity is accentuated by rehabilitation. PMID:26106314

Neural correlates of the object-recall process in semantic memory.

PubMed

Assaf, Michal; Calhoun, Vince D; Kuzu, Cheedem H; Kraut, Michael A; Rivkin, Paul R; Hart, John; Pearlson, Godfrey D

2006-10-30

The recall of an object from features is a specific operation in semantic memory in which the thalamus and pre-supplementary motor area (pre-SMA) are integrally involved. Other higher-order semantic cortices are also likely to be involved. We used the object-recall-from-features paradigm, with more sensitive scanning techniques and larger sample size, to replicate and extend our previous results. Eighteen right-handed healthy participants performed an object-recall task and an association semantic task, while undergoing functional magnetic resonance imaging. During object-recall, subjects determined whether words pairs describing object features combined to recall an object; during the association task they decided if two words were related. Of brain areas specifically involved in object recall, in addition to the thalamus and pre-SMA, other regions included the left dorsolateral prefrontal cortex, inferior parietal lobule, and middle temporal gyrus, and bilateral rostral anterior cingulate and inferior frontal gyri. These regions are involved in semantic processing, verbal working memory and response-conflict detection and monitoring. The thalamus likely helps to coordinate activity of these different brain areas. Understanding the circuit that normally mediates this process is relevant for schizophrenia, where many regions in this circuit are functionally abnormal and semantic memory is impaired.

Methods and Systems for Representing, Using and Displaying Time-Varying Information on the Semantic Web

DTIC Science & Technology

2013-11-26

Combination with Simple Features," lEE European Workshop on Handwriting Analysis and Recognition, pp. 6/1-6, Brussels, Jul. 1994. Bock, J., et a...Document Analysis and Recognition, pp. 147-150, Oct. 1993. Starner, T., eta!., "On-Line Cursive Handwriting Recognition Using Speech Recognition Methods

Sentiments Analysis of Reviews Based on ARCNN Model

NASA Astrophysics Data System (ADS)

Xu, Xiaoyu; Xu, Ming; Xu, Jian; Zheng, Ning; Yang, Tao

2017-10-01

The sentiments analysis of product reviews is designed to help customers understand the status of the product. The traditional method of sentiments analysis relies on the input of a fixed feature vector which is performance bottleneck of the basic codec architecture. In this paper, we propose an attention mechanism with BRNN-CNN model, referring to as ARCNN model. In order to have a good analysis of the semantic relations between words and solves the problem of dimension disaster, we use the GloVe algorithm to train the vector representations for words. Then, ARCNN model is proposed to deal with the problem of deep features training. Specifically, BRNN model is proposed to investigate non-fixed-length vectors and keep time series information perfectly and CNN can study more connection of deep semantic links. Moreover, the attention mechanism can automatically learn from the data and optimize the allocation of weights. Finally, a softmax classifier is designed to complete the sentiment classification of reviews. Experiments show that the proposed method can improve the accuracy of sentiment classification compared with benchmark methods.

Structural and functional correlates for language efficiency in auditory word processing.

PubMed

Jung, JeYoung; Kim, Sunmi; Cho, Hyesuk; Nam, Kichun

2017-01-01

This study aims to provide convergent understanding of the neural basis of auditory word processing efficiency using a multimodal imaging. We investigated the structural and functional correlates of word processing efficiency in healthy individuals. We acquired two structural imaging (T1-weighted imaging and diffusion tensor imaging) and functional magnetic resonance imaging (fMRI) during auditory word processing (phonological and semantic tasks). Our results showed that better phonological performance was predicted by the greater thalamus activity. In contrary, better semantic performance was associated with the less activation in the left posterior middle temporal gyrus (pMTG), supporting the neural efficiency hypothesis that better task performance requires less brain activation. Furthermore, our network analysis revealed the semantic network including the left anterior temporal lobe (ATL), dorsolateral prefrontal cortex (DLPFC) and pMTG was correlated with the semantic efficiency. Especially, this network acted as a neural efficient manner during auditory word processing. Structurally, DLPFC and cingulum contributed to the word processing efficiency. Also, the parietal cortex showed a significate association with the word processing efficiency. Our results demonstrated that two features of word processing efficiency, phonology and semantics, can be supported in different brain regions and, importantly, the way serving it in each region was different according to the feature of word processing. Our findings suggest that word processing efficiency can be achieved by in collaboration of multiple brain regions involved in language and general cognitive function structurally and functionally.

Structural and functional correlates for language efficiency in auditory word processing

PubMed Central

Kim, Sunmi; Cho, Hyesuk; Nam, Kichun

2017-01-01

This study aims to provide convergent understanding of the neural basis of auditory word processing efficiency using a multimodal imaging. We investigated the structural and functional correlates of word processing efficiency in healthy individuals. We acquired two structural imaging (T1-weighted imaging and diffusion tensor imaging) and functional magnetic resonance imaging (fMRI) during auditory word processing (phonological and semantic tasks). Our results showed that better phonological performance was predicted by the greater thalamus activity. In contrary, better semantic performance was associated with the less activation in the left posterior middle temporal gyrus (pMTG), supporting the neural efficiency hypothesis that better task performance requires less brain activation. Furthermore, our network analysis revealed the semantic network including the left anterior temporal lobe (ATL), dorsolateral prefrontal cortex (DLPFC) and pMTG was correlated with the semantic efficiency. Especially, this network acted as a neural efficient manner during auditory word processing. Structurally, DLPFC and cingulum contributed to the word processing efficiency. Also, the parietal cortex showed a significate association with the word processing efficiency. Our results demonstrated that two features of word processing efficiency, phonology and semantics, can be supported in different brain regions and, importantly, the way serving it in each region was different according to the feature of word processing. Our findings suggest that word processing efficiency can be achieved by in collaboration of multiple brain regions involved in language and general cognitive function structurally and functionally. PMID:28892503

Learning Semantic Tags from Big Data for Clinical Text Representation.

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.

Atmosphere-based image classification through luminance and hue

NASA Astrophysics Data System (ADS)

Xu, Feng; Zhang, Yujin

2005-07-01

In this paper a novel image classification system is proposed. Atmosphere serves an important role in generating the scene"s topic or in conveying the message behind the scene"s story, which belongs to abstract attribute level in semantic levels. At first, five atmosphere semantic categories are defined according to rules of photo and film grammar, followed by global luminance and hue features. Then the hierarchical SVM classifiers are applied. In each classification stage, corresponding features are extracted and the trained linear SVM is implemented, resulting in two classes. After three stages of classification, five atmosphere categories are obtained. At last, the text annotation of the atmosphere semantics and the corresponding features by Extensible Markup Language (XML) in MPEG-7 is defined, which can be integrated into more multimedia applications (such as searching, indexing and accessing of multimedia content). The experiment is performed on Corel images and film frames. The classification results prove the effectiveness of the definition of atmosphere semantic classes and the corresponding features.

The use of semantic- and phonological-based feature approaches to treat naming deficits in aphasia.

PubMed

Hashimoto, Naomi

2012-06-01

The aim of the study was to compare approaches highlighting either semantic or phonological features to treat naming deficits in aphasia. Treatment focused on improving picture naming. An alternating treatments design was used with a multiple baseline design across stimuli to examine effects of both approaches in two participants with varying degrees of anomia. The features approaches were modified in that three, rather than six, features were used. Significant differential effects were found across participants; this appeared to be a function of each participant's strengths or preferences over the course of treatment. Modest generalization effects were obtained for one participant. Naming error analyses revealed patterns suggestive of increased lexical access for both participants. These findings provide evidence that using a modified features-based protocol can improve naming when incorporating both semantic and phonological feature cues. Naming error patterns can provide additional evidence of improved naming during treatment.

The Nature and Neural Correlates of Semantic Association versus Conceptual Similarity

PubMed Central

Jackson, Rebecca L.; Hoffman, Paul; Pobric, Gorana; Lambon Ralph, Matthew A.

2015-01-01

The ability to represent concepts and the relationships between them is critical to human cognition. How does the brain code relationships between items that share basic conceptual properties (e.g., dog and wolf) while simultaneously representing associative links between dissimilar items that co-occur in particular contexts (e.g., dog and bone)? To clarify the neural bases of these semantic components in neurologically intact participants, both types of semantic relationship were investigated in an fMRI study optimized for anterior temporal lobe (ATL) coverage. The clear principal finding was that the same core semantic network (ATL, superior temporal sulcus, ventral prefrontal cortex) was equivalently engaged when participants made semantic judgments on the basis of association or conceptual similarity. Direct comparisons revealed small, weaker differences for conceptual similarity > associative decisions (e.g., inferior prefrontal cortex) and associative > conceptual similarity (e.g., ventral parietal cortex) which appear to reflect graded differences in task difficulty. Indeed, once reaction time was entered as a covariate into the analysis, no associative versus category differences remained. The paper concludes with a discussion of how categorical/feature-based and associative relationships might be represented within a single, unified semantic system. PMID:25636912

Vision-based gait impairment analysis for aided diagnosis.

PubMed

Ortells, Javier; Herrero-Ezquerro, María Trinidad; Mollineda, Ramón A

2018-02-12

Gait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk. They were designed to be invariant to frame rate and image size, allowing cross-platform comparisons. Experiments were formulated in terms of two databases. A well-known general-purpose gait dataset is used to establish normal references for features, while a new database, introduced in this work, provides samples under eight different walking styles: one normal and seven impaired patterns. A number of statistical studies were carried out to prove the sensitivity of features at measuring the expected pathologies, providing enough evidence about their accuracy. Graphical Abstract Graphical abstract reflecting main contributions of the manuscript: at the top, a robust, semantic and easy-to-interpret feature set to describe impaired gait patterns; at the bottom, a new dataset consisting of video-recordings of a number of volunteers simulating different patterns of pathological gait, where features were statistically assessed.

A neural network model of semantic memory linking feature-based object representation and words.

PubMed

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

QUINCE System; State-of-the-Art Review

DTIC Science & Technology

1978-06-01

linguistic data base in terms of semantic feature set, interlingual transfer component, contrastive lexical/syntactic studies and contextual analysis ...and Syntactical Studies 3.3.1 Contrastlve Lexical Studies 3.3.2 Contrastlve Syntactic Studies 3.4 Contextual Analysis 3.4.1 Elided Subjects...and English, combined with contextual analysis of language- specific characteristics of Chinese ^re offered as the most promising solutions In this

Ontology patterns for complex topographic feature yypes

USGS Publications Warehouse

Varanka, Dalia E.

2011-01-01

Complex feature types are defined as integrated relations between basic features for a shared meaning or concept. The shared semantic concept is difficult to define in commonly used geographic information systems (GIS) and remote sensing technologies. The role of spatial relations between complex feature parts was recognized in early GIS literature, but had limited representation in the feature or coverage data models of GIS. Spatial relations are more explicitly specified in semantic technology. In this paper, semantics for topographic feature ontology design patterns (ODP) are developed as data models for the representation of complex features. In the context of topographic processes, component assemblages are supported by resource systems and are found on local landscapes. The topographic ontology is organized across six thematic modules that can account for basic feature types, resource systems, and landscape types. Types of complex feature attributes include location, generative processes and physical description. Node/edge networks model standard spatial relations and relations specific to topographic science to represent complex features. To demonstrate these concepts, data from The National Map of the U. S. Geological Survey was converted and assembled into ODP.

A Study of Semantic Features: Electrophysiological Correlates.

ERIC Educational Resources Information Center

Wetzel, Frederick; And Others

This study investigates whether words differing in a single contrastive semantic feature (positive/negative) can be discriminated by auditory evoked responses (AERs). Ten right-handed college students were provided with auditory stimuli consisting of 20 relational words (more/less; high/low, etc.) spoken with a middle American accent and computer…

Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

Face (and Nose) Priming for Book: The Malleability of Semantic Memory

PubMed Central

Coane, Jennifer H.; Balota, David A.

2010-01-01

There are two general classes of models of semantic structure that support semantic priming effects. Feature-overlap models of semantic priming assume that shared features between primes and targets are critical (e.g., cat-DOG). Associative accounts assume that contextual co-occurrence is critical and that the system is organized along associations independent of featural overlap (e.g., leash-DOG). If unrelated concepts can become related as a result of contextual co-occurrence, this would be more supportive of associative accounts and provide insight into the nature of the network underlying “semantic” priming effects. Naturally co-occurring recent associations (e.g., face-BOOK) were tested under conditions that minimize strategic influences (i.e., short stimulus onset asynchrony, low relatedness proportion) in a semantic priming paradigm. Priming for new associations did not differ from the priming found for pre-existing relations (e.g., library-BOOK). Mediated priming (e.g., nose-BOOK) was also found. These results suggest that contextual associations can result in the reorganization of the network that subserves “semantic” priming effects. PMID:20494866

Semantic and visual determinants of face recognition in a prosopagnosic patient.

PubMed

Dixon, M J; Bub, D N; Arguin, M

1998-05-01

Prosopagnosia is the neuropathological inability to recognize familiar people by their faces. It can occur in isolation or can coincide with recognition deficits for other nonface objects. Often, patients whose prosopagnosia is accompanied by object recognition difficulties have more trouble identifying certain categories of objects relative to others. In previous research, we demonstrated that objects that shared multiple visual features and were semantically close posed severe recognition difficulties for a patient with temporal lobe damage. We now demonstrate that this patient's face recognition is constrained by these same parameters. The prosopagnosic patient ELM had difficulties pairing faces to names when the faces shared visual features and the names were semantically related (e.g., Tonya Harding, Nancy Kerrigan, and Josee Chouinard -three ice skaters). He made tenfold fewer errors when the exact same faces were associated with semantically unrelated people (e.g., singer Celine Dion, actress Betty Grable, and First Lady Hillary Clinton). We conclude that prosopagnosia and co-occurring category-specific recognition problems both stem from difficulties disambiguating the stored representations of objects that share multiple visual features and refer to semantically close identities or concepts.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

PubMed Central

Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. PMID:24632078

Semantic attributes for people's appearance description: an appearance modality for video surveillance applications

NASA Astrophysics Data System (ADS)

Frikha, Mayssa; Fendri, Emna; Hammami, Mohamed

2017-09-01

Using semantic attributes such as gender, clothes, and accessories to describe people's appearance is an appealing modeling method for video surveillance applications. We proposed a midlevel appearance signature based on extracting a list of nameable semantic attributes describing the body in uncontrolled acquisition conditions. Conventional approaches extract the same set of low-level features to learn the semantic classifiers uniformly. Their critical limitation is the inability to capture the dominant visual characteristics for each trait separately. The proposed approach consists of extracting low-level features in an attribute-adaptive way by automatically selecting the most relevant features for each attribute separately. Furthermore, relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations. We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions. These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes. Various experiments prove that carefully designed features for learning the visual characteristics for an attribute provide an improvement of the correct classification accuracy and a reduction of both spatial and temporal complexities against state-of-the-art approaches.

Individual differences in automatic semantic priming.

PubMed

Andrews, Sally; Lo, Steson; Xia, Violet

2017-05-01

This research investigated whether masked semantic priming in a semantic categorization task that required classification of words as animals or nonanimals was modulated by individual differences in lexical proficiency. A sample of 89 skilled readers, assessed on reading comprehension, vocabulary and spelling ability, classified target words preceded by brief (50 ms) masked primes that were either congruent or incongruent with the category of the target. Congruent primes were also selected to be either high (e.g., hawk EAGLE, pistol RIFLE) or low (e.g., mole EAGLE, boots RIFLE) in semantic feature overlap with the target. "Overall proficiency," indexed by high performance on both a "semantic composite" measure of reading comprehension and vocabulary and a "spelling composite," was associated with stronger congruence priming from both high and low feature overlap primes for animal exemplars, but only predicted priming from low overlap primes for nonexemplars. Classification of high frequency nonexemplars was also significantly modulated by an independent "spelling-meaning" factor, indexed by the discrepancy between the semantic and spelling composites, because relatively higher scores on the semantic than the spelling composite were associated with stronger semantic priming. These findings show that higher lexical proficiency is associated with stronger evidence of automatic semantic priming and suggest that individual differences in lexical quality modulate the division of labor between orthographic and semantic processing in early lexical retrieval. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The neural correlates of semantic richness: evidence from an fMRI study of word learning.

PubMed

Ferreira, Roberto A; Göbel, Silke M; Hymers, Mark; Ellis, Andrew W

2015-04-01

We investigated the neural correlates of concrete nouns with either many or few semantic features. A group of 21 participants underwent two days of training and were then asked to categorize 40 newly learned words and a set of matched familiar words as living or nonliving in an MRI scanner. Our results showed that the most reliable effects of semantic richness were located in the left angular gyrus (AG) and middle temporal gyrus (MTG), where activation was higher for semantically rich than poor words. Other areas showing the same pattern included bilateral precuneus and posterior cingulate gyrus. Our findings support the view that AG and anterior MTG, as part of the multimodal network, play a significant role in representing and integrating semantic features from different input modalities. We propose that activation in bilateral precuneus and posterior cingulate gyrus reflects interplay between AG and episodic memory systems during semantic retrieval. Copyright © 2015 Elsevier Inc. All rights reserved.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

What Research Says about Vocabulary Instruction for Students with Learning Disabilities

ERIC Educational Resources Information Center

Jitendra, Asha K.; Edwards, Lana L.; Sacks, Gabriell; Jacobson, Lisa A.

2004-01-01

This article summarizes published research on vocabulary instruction involving students with learning disabilities. Nineteen vocabulary studies that comprised 27 investigations were located. Study interventions gleaned from the review included keyword or mnemonic approaches, cognitive strategy instruction (e.g., semantic features analysis), direct…

Here's Looking at You: Visual Similarity Exacerbates the Moses Illusion for Semantically Similar Celebrities

ERIC Educational Resources Information Center

Davis, Danielle K.; Abrams, Lise

2016-01-01

When people read questions like "How many animals of each kind did Moses take on the ark?", many mistakenly answer "2" despite knowing that Noah sailed the ark. This "Moses illusion" occurs when names share semantic features. Two experiments examined whether shared "visual" concepts (facial features)…

Semantic Enhancement for Enterprise Data Management

NASA Astrophysics Data System (ADS)

Ma, Li; Sun, Xingzhi; Cao, Feng; Wang, Chen; Wang, Xiaoyuan; Kanellos, Nick; Wolfson, Dan; Pan, Yue

Taking customer data as an example, the paper presents an approach to enhance the management of enterprise data by using Semantic Web technologies. Customer data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer data management (CDM) is becoming critical for enterprises because it keeps a single, complete and accurate record of customers across the enterprise. Existing CDM systems focus on integrating customer data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and data management community.

Shared neural processes support semantic control and action understanding

PubMed Central

Davey, James; Rueschemeyer, Shirley-Ann; Costigan, Alison; Murphy, Nik; Krieger-Redwood, Katya; Hallam, Glyn; Jefferies, Elizabeth

2015-01-01

Executive–semantic control and action understanding appear to recruit overlapping brain regions but existing evidence from neuroimaging meta-analyses and neuropsychology lacks spatial precision; we therefore manipulated difficulty and feature type (visual vs. action) in a single fMRI study. Harder judgements recruited an executive–semantic network encompassing medial and inferior frontal regions (including LIFG) and posterior temporal cortex (including pMTG). These regions partially overlapped with brain areas involved in action but not visual judgements. In LIFG, the peak responses to action and difficulty were spatially identical across participants, while these responses were overlapping yet spatially distinct in posterior temporal cortex. We propose that the co-activation of LIFG and pMTG allows the flexible retrieval of semantic information, appropriate to the current context; this might be necessary both for semantic control and understanding actions. Feature selection in difficult trials also recruited ventral occipital–temporal areas, not implicated in action understanding. PMID:25658631

SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coroller, T; Bi, W; Abedalthagafi, M

Purpose: The clinical management of meningioma is guided by its grade and biologic behavior. Currently, diagnosis of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor behavior are needed. We investigated the association between imaging features extracted from preoperative gadolinium-enhanced T1-weighted MRI and meningioma grade. Methods: We retrospectively examined the pre-operative MRI for 139 patients with de novo WHO grade I (63%) and grade II (37%) meningiomas. We investigated the predictive power of ten semantic radiologic features as determined by a neuroradiologist, fifteen radiomic features, and tumor location. Conventional (volume and diameter) imaging featuresmore » were added for comparison. AUC was computed for continuous and χ{sup 2} for discrete variables. Classification was done using random forest. Performance was evaluated using cross validation (1000 iterations, 75% training and 25% validation). All p-values were adjusted for multiple testing. Results: Significant association was observed between meningioma grade and tumor location (p<0.001) and two semantic features including intra-tumoral heterogeneity (p<0.001) and overt hemorrhage (p=0.01). Conventional (AUC 0.61–0.67) and eleven radiomic (AUC 0.60–0.70) features were significant from random (p<0.05, Noether test). Median AUC values for classification of tumor grade were 0.57, 0.71, 0.72 and 0.77 respectively for conventional, radiomic, location, and semantic features after using random forest. By combining all imaging data (semantic, radiomic, and location), the median AUC was 0.81, which offers superior predicting power to that of conventional imaging descriptors for meningioma as well as radiomic features alone (p<0.05, permutation test). Conclusion: We demonstrate a strong association between radiologic features and meningioma grade. Pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.« less

Semantic Features for Classifying Referring Search Terms

DOE Office of Scientific and Technical Information (OSTI.GOV)

May, Chandler J.; Henry, Michael J.; McGrath, Liam R.

2012-05-11

When an internet user clicks on a result in a search engine, a request is submitted to the destination web server that includes a referrer field containing the search terms given by the user. Using this information, website owners can analyze the search terms leading to their websites to better understand their visitors needs. This work explores some of the features that can be used for classification-based analysis of such referring search terms. We present initial results for the example task of classifying HTTP requests countries of origin. A system that can accurately predict the country of origin from querymore » text may be a valuable complement to IP lookup methods which are susceptible to the obfuscation of dereferrers or proxies. We suggest that the addition of semantic features improves classifier performance in this example application. We begin by looking at related work and presenting our approach. After describing initial experiments and results, we discuss paths forward for this work.« less

Lexical and semantic ability in groups of children with cochlear implants, language impairment and autism spectrum disorder.

PubMed

Löfkvist, Ulrika; Almkvist, Ove; Lyxell, Björn; Tallberg, Ing-Mari

2014-02-01

Lexical-semantic ability was investigated among children aged 6-9 years with cochlear implants (CI) and compared to clinical groups of children with language impairment (LI) and autism spectrum disorder (ASD) as well as to age-matched children with normal hearing (NH). In addition, the influence of age at implantation on lexical-semantic ability was investigated among children with CI. 97 children divided into four groups participated, CI (n=34), LI (n=12), ASD (n=12), and NH (n=39). A battery of tests, including picture naming, receptive vocabulary and knowledge of semantic features, was used for assessment. A semantic response analysis of the erroneous responses on the picture-naming test was also performed. The group of children with CI exhibited a naming ability comparable to that of the age-matched children with NH, and they also possessed a relevant semantic knowledge of certain words that they were unable to name correctly. Children with CI had a significantly better understanding of words compared to the children with LI and ASD, but a worse understanding than those with NH. The significant differences between groups remained after controlling for age and non-verbal cognitive ability. The children with CI demonstrated lexical-semantic abilities comparable to age-matched children with NH, while children with LI and ASD had a more atypical lexical-semantic profile and poorer sizes of expressive and receptive vocabularies. Dissimilar causes of neurodevelopmental processes seemingly affected lexical-semantic abilities in different ways in the clinical groups. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Classification of clinically useful sentences in clinical evidence resources.

PubMed

Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana; Jonnalagadda, Siddhartha R; Del Fiol, Guilherme

2016-04-01

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. Copyright © 2016 Elsevier Inc. All rights reserved.

Double dissociation of semantic categories in Alzheimer's disease.

PubMed

Gonnerman, L M; Andersen, E S; Devlin, J T; Kempler, D; Seidenberg, M S

1997-04-01

Data that demonstrate distinct patterns of semantic impairment in Alzheimer's disease (AD) are presented. Findings suggest that while groups of mild-moderate patients may not display category specific impairments, some individual patients do show selective impairment of either natural kinds or artifacts. We present a model of semantic organization in which category specific impairments arise from damage to distributed features underlying different types of categories. We incorporate the crucial notions of intercorrelations and distinguishing features, allowing us to demonstrate (1) how category specific impairments can result from widespread damage and (2) how selective deficits in AD reflect different points in the progression of impairment. The different patterns of impairment arise from an interaction between the nature of the semantic categories and the progression of damage.

A Practical Approach to Vocabulary Reinforcement.

ERIC Educational Resources Information Center

Stieglitz, Ezra L.

1983-01-01

Techniques of semantic feature analysis are applied to exploration and reinforcement of vocabulary. Students are presented with categories of familiar items and asked to describe their characteristics. The method can be used to elicit sentences, reinforce existing vocabulary, and begin discussion. Sample exercises for several difficulty levels are…

Sentiment Analysis Using Common-Sense and Context Information

PubMed Central

Mittal, Namita; Bansal, Pooja; Garg, Sonal

2015-01-01

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods. PMID:25866505

Sentiment analysis using common-sense and context information.

PubMed

Agarwal, Basant; Mittal, Namita; Bansal, Pooja; Garg, Sonal

2015-01-01

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

Analyticity and Features of Semantic Interaction.

ERIC Educational Resources Information Center

Steinberg, Danny D.

The findings reported in this paper are the result of an experiment to determine the empirical validity of such semantic concepts as analytic, synthetic, and contradictory. Twenty-eight university students were presented with 156 sentences to assign to one of four semantic categories: (1) synthetic ("The dog is a poodle"), (2) analytic…

The Role of Semantic Features in Verb Processing

ERIC Educational Resources Information Center

Bonnotte, Isabelle

2008-01-01

The present study examined the general hypothesis that, as for nouns, stable representations of semantic knowledge relative to situations expressed by verbs are available and accessible in long term memory in normal people. Regular associations between verbs and past tenses in French adults allowed to abstract two superordinate semantic features…

Heteromodal Cortical Areas Encode Sensory-Motor Features of Word Meaning.

PubMed

Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Seidenberg, Mark S; Binder, Jeffrey R

2016-09-21

The capacity to process information in conceptual form is a fundamental aspect of human cognition, yet little is known about how this type of information is encoded in the brain. Although the role of sensory and motor cortical areas has been a focus of recent debate, neuroimaging studies of concept representation consistently implicate a network of heteromodal areas that seem to support concept retrieval in general rather than knowledge related to any particular sensory-motor content. We used predictive machine learning on fMRI data to investigate the hypothesis that cortical areas in this "general semantic network" (GSN) encode multimodal information derived from basic sensory-motor processes, possibly functioning as convergence-divergence zones for distributed concept representation. An encoding model based on five conceptual attributes directly related to sensory-motor experience (sound, color, shape, manipulability, and visual motion) was used to predict brain activation patterns associated with individual lexical concepts in a semantic decision task. When the analysis was restricted to voxels in the GSN, the model was able to identify the activation patterns corresponding to individual concrete concepts significantly above chance. In contrast, a model based on five perceptual attributes of the word form performed at chance level. This pattern was reversed when the analysis was restricted to areas involved in the perceptual analysis of written word forms. These results indicate that heteromodal areas involved in semantic processing encode information about the relative importance of different sensory-motor attributes of concepts, possibly by storing particular combinations of sensory and motor features. The present study used a predictive encoding model of word semantics to decode conceptual information from neural activity in heteromodal cortical areas. The model is based on five sensory-motor attributes of word meaning (color, shape, sound, visual motion, and manipulability) and encodes the relative importance of each attribute to the meaning of a word. This is the first demonstration that heteromodal areas involved in semantic processing can discriminate between different concepts based on sensory-motor information alone. This finding indicates that the brain represents concepts as multimodal combinations of sensory and motor representations. Copyright © 2016 the authors 0270-6474/16/369763-07$15.00/0.

Abnormal object recall and anterior cingulate overactivation correlate with formal thought disorder in schizophrenia.

PubMed

Assaf, Michal; Rivkin, Paul R; Kuzu, Cheedem H; Calhoun, Vince D; Kraut, Michael A; Groth, Karyn M; Yassa, Michael A; Hart, John; Pearlson, Godfrey D

2006-03-01

The neural basis of formal thought disorder (FTD) is unknown. An influential theory is that FTD results from impaired semantic memory processing. We explored the neural correlates of semantic memory retrieval in schizophrenia using an imaging task assessing semantic object recall. Sixteen healthy control subjects and sixteen schizophrenia patients whose FTD symptoms were measured with the Thought Disorder Index completed a verbal object-recall task during functional magnetic resonance imaging. Participants viewed two words describing object features that either evoked (object recall) or did not evoke a semantic concept. Schizophrenia patients tended to overrecall objects for feature pairs that did not describe the same object. Functionally, rostral anterior cingulate cortex (ACC) activation in patients positively correlated with FTD severity during both correct recalled and overrecalled trials. Compared with control subjects, during object recalling, patients overactivated bilateral ACC, temporooccipital junctions, temporal poles and parahippocampi, right inferior frontal gyrus, and dorsolateral prefrontal cortex, but underactivated inferior parietal lobules. Our results support impaired semantic memory retrieval as underlying FTD pathophysiology. Schizophrenia patients showed abnormal activations of brain areas involved in semantic memory, verbal working memory, and initiation and suppression of conflicting responses, which were associated with semantic overrecall and FTD.

Scene Semantic Segmentation from Indoor Rgb-D Images Using Encode-Decoder Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Wang, Z.; Li, T.; Pan, L.; Kang, Z.

2017-09-01

With increasing attention for the indoor environment and the development of low-cost RGB-D sensors, indoor RGB-D images are easily acquired. However, scene semantic segmentation is still an open area, which restricts indoor applications. The depth information can help to distinguish the regions which are difficult to be segmented out from the RGB images with similar color or texture in the indoor scenes. How to utilize the depth information is the key problem of semantic segmentation for RGB-D images. In this paper, we propose an Encode-Decoder Fully Convolutional Networks for RGB-D image classification. We use Multiple Kernel Maximum Mean Discrepancy (MK-MMD) as a distance measure to find common and special features of RGB and D images in the network to enhance performance of classification automatically. To explore better methods of applying MMD, we designed two strategies; the first calculates MMD for each feature map, and the other calculates MMD for whole batch features. Based on the result of classification, we use the full connect CRFs for the semantic segmentation. The experimental results show that our method can achieve a good performance on indoor RGB-D image semantic segmentation.

Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features

PubMed Central

Zhang, Yaoyun; Jiang, Min; Wang, Jingqi; Xu, Hua

2016-01-01

Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926

The flanker compatibility effect as a function of visual angle, attentional focus, visual transients, and perceptual load: a search for boundary conditions.

PubMed

Miller, J

1991-03-01

When subjects must respond to a relevant center letter and ignore irrelevant flanking letters, the identities of the flankers produce a response compatibility effect, indicating that they are processed semantically at least to some extent. Because this effect decreases as the separation between target and flankers increases, the effect appears to result from imperfect early selection (attenuation). In the present experiments, several features of the focused attention paradigm were examined, in order to determine whether they might produce the flanker compatibility effect by interfering with the operation of an early selective mechanism. Specifically, the effect might be produced because the paradigm requires subjects to (1) attend exclusively to stimuli within a very small visual angle, (2) maintain a long-term attentional focus on a constant display location, (3) focus attention on an empty display location, (4) exclude onset-transient flankers from semantic processing, or (5) ignore some of the few stimuli in an impoverished visual field. The results indicate that none of these task features is required for semantic processing of unattended stimuli to occur. In fact, visual angle is the only one of the task features that clearly has a strong influence on the size of the flanker compatibility effect. The invariance of the flanker compatibility effect across these conditions suggests that the mechanism for early selection rarely, if ever, completely excludes unattended stimuli from semantic analysis. In addition, it shows that selective mechanisms are relatively insensitive to several factors that might be expected to influence them, thereby supporting the view that spatial separation has a special status for visual selective attention.

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

Semantics vs Pragmatics of a Compound Word

ERIC Educational Resources Information Center

Smirnova, Elena A.; Biktemirova, Ella I.; Davletbaeva, Diana N.

2016-01-01

This paper is devoted to the study of correlation between semantic and pragmatic potential of a compound word, which functions in informal speech, and the mechanisms of secondary nomination, which realizes the potential of semantic-pragmatic features of colloquial compounds. The relevance and the choice of the research question is based on the…

Improving EFL Writing through Study of Semantic Concepts in Formulaic Language

ERIC Educational Resources Information Center

Schenck, Andrew D.; Choi, Wonkyung

2015-01-01

Within Asian EFL contexts such as South Korea, large class sizes, poor sources of input and an overreliance on the Grammar-Translation Method may negatively impact semantic and pragmatic development of writing content. Since formulaic language is imbued with syntactic, semantic and pragmatic linguistic features, it represents an ideal means to…

A novel co-occurrence-based approach to predict pure associative and semantic priming.

PubMed

Roelke, Andre; Franke, Nicole; Biemann, Chris; Radach, Ralph; Jacobs, Arthur M; Hofmann, Markus J

2018-03-15

The theoretical "difficulty in separating association strength from [semantic] feature overlap" has resulted in inconsistent findings of either the presence or absence of "pure" associative priming in recent literature (Hutchison, 2003, Psychonomic Bulletin & Review, 10(4), p. 787). The present study used co-occurrence statistics of words in sentences to provide a full factorial manipulation of direct association (strong/no) and the number of common associates (many/no) of the prime and target words. These common associates were proposed to serve as semantic features for a recent interactive activation model of semantic processing (i.e., the associative read-out model; Hofmann & Jacobs, 2014). With stimulus onset asynchrony (SOA) as an additional factor, our findings indicate that associative and semantic priming are indeed dissociable. Moreover, the effect of direct association was strongest at a long SOA (1,000 ms), while many common associates facilitated lexical decisions primarily at a short SOA (200 ms). This response pattern is consistent with previous performance-based accounts and suggests that associative and semantic priming can be evoked by computationally determined direct and common associations.

Frontotemporal neural systems supporting semantic processing in Alzheimer's disease.

PubMed

Peelle, Jonathan E; Powers, John; Cook, Philip A; Smith, Edward E; Grossman, Murray

2014-03-01

We hypothesized that semantic memory for object concepts involves both representations of visual feature knowledge in modality-specific association cortex and heteromodal regions that are important for integrating and organizing this semantic knowledge so that it can be used in a flexible, contextually appropriate manner. We examined this hypothesis in an fMRI study of mild Alzheimer's disease (AD). Participants were presented with pairs of printed words and asked whether the words matched on a given visual-perceptual feature (e.g., guitar, violin: SHAPE). The stimuli probed natural kinds and manufactured objects, and the judgments involved shape or color. We found activation of bilateral ventral temporal cortex and left dorsolateral prefrontal cortex during semantic judgments, with AD patients showing less activation of these regions than healthy seniors. Moreover, AD patients showed less ventral temporal activation than did healthy seniors for manufactured objects, but not for natural kinds. We also used diffusion-weighted MRI of white matter to examine fractional anisotropy (FA). Patients with AD showed significantly reduced FA in the superior longitudinal fasciculus and inferior frontal-occipital fasciculus, which carry projections linking temporal and frontal regions of this semantic network. Our results are consistent with the hypothesis that semantic memory is supported in part by a large-scale neural network involving modality-specific association cortex, heteromodal association cortex, and projections between these regions. The semantic deficit in AD thus arises from gray matter disease that affects the representation of feature knowledge and processing its content, as well as white matter disease that interrupts the integrated functioning of this large-scale network.

Joint modality fusion and temporal context exploitation for semantic video analysis

NASA Astrophysics Data System (ADS)

Papadopoulos, Georgios Th; Mezaris, Vasileios; Kompatsiaris, Ioannis; Strintzis, Michael G.

2011-12-01

In this paper, a multi-modal context-aware approach to semantic video analysis is presented. Overall, the examined video sequence is initially segmented into shots and for every resulting shot appropriate color, motion and audio features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing an initial association of each shot with the semantic classes that are of interest separately for each modality. Subsequently, a graphical modeling-based approach is proposed for jointly performing modality fusion and temporal context exploitation. Novelties of this work include the combined use of contextual information and multi-modal fusion, and the development of a new representation for providing motion distribution information to HMMs. Specifically, an integrated Bayesian Network is introduced for simultaneously performing information fusion of the individual modality analysis results and exploitation of temporal context, contrary to the usual practice of performing each task separately. Contextual information is in the form of temporal relations among the supported classes. Additionally, a new computationally efficient method for providing motion energy distribution-related information to HMMs, which supports the incorporation of motion characteristics from previous frames to the currently examined one, is presented. The final outcome of this overall video analysis framework is the association of a semantic class with every shot. Experimental results as well as comparative evaluation from the application of the proposed approach to four datasets belonging to the domains of tennis, news and volleyball broadcast video are presented.

Are Automatic Conceptual Cores the Gold Standard of Semantic Processing? The Context-Dependence of Spatial Meaning in Grounded Congruency Effects

ERIC Educational Resources Information Center

Lebois, Lauren A. M.; Wilson-Mendenhall, Christine D.; Barsalou, Lawrence W.

2015-01-01

According to grounded cognition, words whose semantics contain sensory-motor features activate sensory-motor simulations, which, in turn, interact with spatial responses to produce grounded congruency effects (e.g., processing the spatial feature of "up" for sky should be faster for up vs. down responses). Growing evidence shows these…

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

Activating representations in permanent memory: different benefits for pictures and words.

PubMed

Seifert, L S

1997-09-01

Previous research has suggested that pictures have privileged access to semantic memory (W. R. Glaser, 1992), but J. Theios and P. C. Amrhein (1989b) argued that prior studies inappropriately used large pictures and small words. In Experiment 1, participants categorized pictures reliably faster than words, even when both types of items were of optimal perceptual size. In Experiment 2, a poststimulus flashmask and judgments about internal features did not eliminate picture superiority, indicating that it was not due to differences in early visual processing or analysis of visible features. In Experiment 3, when participants made judgments about whether items were related, latencies were reliably faster for categorically related pictures than for words, but there was no picture advantage for noncategorically associated items. Results indicate that pictures have privileged access to semantic memory for categories, but that neither pictures nor words seem to have privileged access to noncategorical associations.

Enhancing clinical concept extraction with distributional semantics

PubMed Central

Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

2011-01-01

Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged f-measure for exact match increased from 80.3% to 82.3% and the micro-averaged f-measure based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data. PMID:22085698

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Semantic image segmentation with fused CNN features

NASA Astrophysics Data System (ADS)

Geng, Hui-qiang; Zhang, Hua; Xue, Yan-bing; Zhou, Mian; Xu, Guang-ping; Gao, Zan

2017-09-01

Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network (CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field (CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively.

Effects of semantic neighborhood density in abstract and concrete words.

PubMed

Reilly, Megan; Desai, Rutvik H

2017-12-01

Concrete and abstract words are thought to differ along several psycholinguistic variables, such as frequency and emotional content. Here, we consider another variable, semantic neighborhood density, which has received much less attention, likely because semantic neighborhoods of abstract words are difficult to measure. Using a corpus-based method that creates representations of words that emphasize featural information, the current investigation explores the relationship between neighborhood density and concreteness in a large set of English nouns. Two important observations emerge. First, semantic neighborhood density is higher for concrete than for abstract words, even when other variables are accounted for, especially for smaller neighborhood sizes. Second, the effects of semantic neighborhood density on behavior are different for concrete and abstract words. Lexical decision reaction times are fastest for words with sparse neighborhoods; however, this effect is stronger for concrete words than for abstract words. These results suggest that semantic neighborhood density plays a role in the cognitive and psycholinguistic differences between concrete and abstract words, and should be taken into account in studies involving lexical semantics. Furthermore, the pattern of results with the current feature-based neighborhood measure is very different from that with associatively defined neighborhoods, suggesting that these two methods should be treated as separate measures rather than two interchangeable measures of semantic neighborhoods. Copyright © 2017 Elsevier B.V. All rights reserved.

Semantic attributes based texture generation

NASA Astrophysics Data System (ADS)

Chi, Huifang; Gan, Yanhai; Qi, Lin; Dong, Junyu; Madessa, Amanuel Hirpa

2018-04-01

Semantic attributes are commonly used for texture description. They can be used to describe the information of a texture, such as patterns, textons, distributions, brightness, and so on. Generally speaking, semantic attributes are more concrete descriptors than perceptual features. Therefore, it is practical to generate texture images from semantic attributes. In this paper, we propose to generate high-quality texture images from semantic attributes. Over the last two decades, several works have been done on texture synthesis and generation. Most of them focusing on example-based texture synthesis and procedural texture generation. Semantic attributes based texture generation still deserves more devotion. Gan et al. proposed a useful joint model for perception driven texture generation. However, perceptual features are nonobjective spatial statistics used by humans to distinguish different textures in pre-attentive situations. To give more describing information about texture appearance, semantic attributes which are more in line with human description habits are desired. In this paper, we use sigmoid cross entropy loss in an auxiliary model to provide enough information for a generator. Consequently, the discriminator is released from the relatively intractable mission of figuring out the joint distribution of condition vectors and samples. To demonstrate the validity of our method, we compare our method to Gan et al.'s method on generating textures by designing experiments on PTD and DTD. All experimental results show that our model can generate textures from semantic attributes.

Does the Sound of a Barking Dog Activate its Corresponding Visual Form? An fMRI Investigation of Modality-Specific Semantic Access

PubMed Central

Reilly, Jamie; Garcia, Amanda; Binney, Richard J.

2016-01-01

Much remains to be learned about the neural architecture underlying word meaning. Fully distributed models of semantic memory predict that the sound of a barking dog will conjointly engage a network of distributed sensorimotor spokes. An alternative framework holds that modality-specific features additionally converge within transmodal hubs. Participants underwent functional MRI while covertly naming familiar objects versus newly learned novel objects from only one of their constituent semantic features (visual form, characteristic sound, or point-light motion representation). Relative to the novel object baseline, familiar concepts elicited greater activation within association regions specific to that presentation modality. Furthermore, visual form elicited activation within high-level auditory association cortex. Conversely, environmental sounds elicited activation in regions proximal to visual association cortex. Both conditions commonly engaged a putative hub region within lateral anterior temporal cortex. These results support hybrid semantic models in which local hubs and distributed spokes are dually engaged in service of semantic memory. PMID:27289210

Taxonomic and Thematic Semantic Systems

PubMed Central

Mirman, Daniel; Landrigan, Jon-Frederick; Britt, Allison E.

2017-01-01

Object concepts are critical for nearly all aspects of human cognition, from perception tasks like object recognition, to understanding and producing language, to making meaningful actions. Concepts can have two very different kinds of relations: similarity relations based on shared features (e.g., dog – bear), which are called “taxonomic” relations, and contiguity relations based on co-occurrence in events or scenarios (e.g., dog – leash), which are called “thematic” relations. Here we report a systematic review of experimental psychology and cognitive neuroscience evidence of this distinction in the structure of semantic memory. We propose two principles that may drive the development of distinct taxonomic and thematic semantic systems: (1) differences between which features determine taxonomic vs. thematic relations and (2) differences in the processing required to extract taxonomic vs. thematic relations. This review brings together distinct threads of behavioral, computational, and neuroscience research on semantic memory in support of a functional and neural dissociation, and defines a framework for future studies of semantic memory. PMID:28333494

Word Recognition is Affected by the Meaning of Orthographic Neighbours: Evidence from Semantic Decision Tasks

ERIC Educational Resources Information Center

Boot, Inge; Pecher, Diane

2008-01-01

Many models of word recognition predict that neighbours of target words will be activated during word processing. Cascaded models can make the additional prediction that semantic features of those neighbours get activated before the target has been uniquely identified. In two semantic decision tasks neighbours that were congruent (i.e., from the…

Semantic Relevance, Domain Specificity and the Sensory/Functional Theory of Category-Specificity

ERIC Educational Resources Information Center

Sartori, Giuseppe; Gnoato, Francesca; Mariani, Ilenia; Prioni, Sara; Lombardi, Luigi

2007-01-01

According to the sensory/functional theory of semantic memory, Living items rely more on Sensory knowledge than Non-living ones. The sensory/functional explanation of category-specificity assumes that semantic features are organised on the basis of their content. We report here a study on DAT patients with impaired performance on Living items and…

SemantGeo: Powering Ecological and Environment Data Discovery and Search with Standards-Based Geospatial Reasoning

NASA Astrophysics Data System (ADS)

Seyed, P.; Ashby, B.; Khan, I.; Patton, E. W.; McGuinness, D. L.

2013-12-01

Recent efforts to create and leverage standards for geospatial data specification and inference include the GeoSPARQL standard, Geospatial OWL ontologies (e.g., GAZ, Geonames), and RDF triple stores that support GeoSPARQL (e.g., AllegroGraph, Parliament) that use RDF instance data for geospatial features of interest. However, there remains a gap on how best to fuse software engineering best practices and GeoSPARQL within semantic web applications to enable flexible search driven by geospatial reasoning. In this abstract we introduce the SemantGeo module for the SemantEco framework that helps fill this gap, enabling scientists find data using geospatial semantics and reasoning. SemantGeo provides multiple types of geospatial reasoning for SemantEco modules. The server side implementation uses the Parliament SPARQL Endpoint accessed via a Tomcat servlet. SemantGeo uses the Google Maps API for user-specified polygon construction and JsTree for providing containment and categorical hierarchies for search. SemantGeo uses GeoSPARQL for spatial reasoning alone and in concert with RDFS/OWL reasoning capabilities to determine, e.g., what geofeatures are within, partially overlap with, or within a certain distance from, a given polygon. We also leverage qualitative relationships defined by the Gazetteer ontology that are composites of spatial relationships as well as administrative designations or geophysical phenomena. We provide multiple mechanisms for exploring data, such as polygon (map-based) and named-feature (hierarchy-based) selection, that enable flexible search constraints using boolean combination of selections. JsTree-based hierarchical search facets present named features and include a 'part of' hierarchy (e.g., measurement-site-01, Lake George, Adirondack Region, NY State) and type hierarchies (e.g., nodes in the hierarchy for WaterBody, Park, MeasurementSite), depending on the ';axis of choice' option selected. Using GeoSPARQL and aforementioned ontology, these hierarchies are constrained based on polygon selection, where the corresponding polygons of the contained features are visually rendered to assist exploration. Once measurement sites are plotted based on initial search, subsequent searches using JsTree selections can extend the previous based on nearby waterbodies in some semantic relationship of interest. For example, ';tributary of' captures water bodies that flow into the current one, and extending the original search to include tributaries of the observed water body is useful to environmental scientists for isolating the source of characteristic levels, including pollutants. Ultimately any SemantEco module can leverage SemantGeo's underlying APIs, leveraged in a deployment of SemantEco that combines EPA and USGS water quality data, and one customized for searching data available from the Darrin Freshwater Institute. Future work will address generating RDF geometry data from shape files, aligning RDF data sources to better leverage qualitative and spatial relationships, and validating newly generated RDF data adhering to the GeoSPARQL standard.

Multiple Regions of a Cortical Network Commonly Encode the Meaning of Words in Multiple Grammatical Positions of Read Sentences.

PubMed

Anderson, Andrew James; Lalor, Edmund C; Lin, Feng; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Raizada, Rajeev D S; Grimm, Scott; Wang, Xixi

2018-05-16

Deciphering how sentence meaning is represented in the brain remains a major challenge to science. Semantically related neural activity has recently been shown to arise concurrently in distributed brain regions as successive words in a sentence are read. However, what semantic content is represented by different regions, what is common across them, and how this relates to words in different grammatical positions of sentences is weakly understood. To address these questions, we apply a semantic model of word meaning to interpret brain activation patterns elicited in sentence reading. The model is based on human ratings of 65 sensory/motor/emotional and cognitive features of experience with words (and their referents). Through a process of mapping functional Magnetic Resonance Imaging activation back into model space we test: which brain regions semantically encode content words in different grammatical positions (e.g., subject/verb/object); and what semantic features are encoded by different regions. In left temporal, inferior parietal, and inferior/superior frontal regions we detect the semantic encoding of words in all grammatical positions tested and reveal multiple common components of semantic representation. This suggests that sentence comprehension involves a common core representation of multiple words' meaning being encoded in a network of regions distributed across the brain.

Selective Attention to Semantic and Syntactic Features Modulates Sentence Processing Networks in Anterior Temporal Cortex

PubMed Central

Rogalsky, Corianne

2009-01-01

Numerous studies have identified an anterior temporal lobe (ATL) region that responds preferentially to sentence-level stimuli. It is unclear, however, whether this activity reflects a response to syntactic computations or some form of semantic integration. This distinction is difficult to investigate with the stimulus manipulations and anomaly detection paradigms traditionally implemented. The present functional magnetic resonance imaging study addresses this question via a selective attention paradigm. Subjects monitored for occasional semantic anomalies or occasional syntactic errors, thus directing their attention to semantic integration, or syntactic properties of the sentences. The hemodynamic response in the sentence-selective ATL region (defined with a localizer scan) was examined during anomaly/error-free sentences only, to avoid confounds due to error detection. The majority of the sentence-specific region of interest was equally modulated by attention to syntactic or compositional semantic features, whereas a smaller subregion was only modulated by the semantic task. We suggest that the sentence-specific ATL region is sensitive to both syntactic and integrative semantic functions during sentence processing, with a smaller portion of this area preferentially involved in the later. This study also suggests that selective attention paradigms may be effective tools to investigate the functional diversity of networks involved in sentence processing. PMID:18669589

Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach

NASA Astrophysics Data System (ADS)

Du, Shihong; Zhang, Fangli; Zhang, Xiuyuan

2015-07-01

While most existing studies have focused on extracting geometric information on buildings, only a few have concentrated on semantic information. The lack of semantic information cannot satisfy many demands on resolving environmental and social issues. This study presents an approach to semantically classify buildings into much finer categories than those of existing studies by learning random forest (RF) classifier from a large number of imbalanced samples with high-dimensional features. First, a two-level segmentation mechanism combining GIS and VHR image produces single image objects at a large scale and intra-object components at a small scale. Second, a semi-supervised method chooses a large number of unbiased samples by considering the spatial proximity and intra-cluster similarity of buildings. Third, two important improvements in RF classifier are made: a voting-distribution ranked rule for reducing the influences of imbalanced samples on classification accuracy and a feature importance measurement for evaluating each feature's contribution to the recognition of each category. Fourth, the semantic classification of urban buildings is practically conducted in Beijing city, and the results demonstrate that the proposed approach is effective and accurate. The seven categories used in the study are finer than those in existing work and more helpful to studying many environmental and social problems.

Discourse Factors in the Evaluation of Language Ability.

ERIC Educational Resources Information Center

Litteral, Robert

Features of connected discourse that have been identified by discourse analysis may be applied to the evaluation of oral proficiency in a second language. For example, in the area of semantics, a speaker's control of the cause-result relationship involves, among other things, the ability to produce the different grammatical and lexical…

The semantic richness of abstract concepts

PubMed Central

Recchia, Gabriel; Jones, Michael N.

2012-01-01

We contrasted the predictive power of three measures of semantic richness—number of features (NFs), contextual dispersion (CD), and a novel measure of number of semantic neighbors (NSN)—for a large set of concrete and abstract concepts on lexical decision and naming tasks. NSN (but not NF) facilitated processing for abstract concepts, while NF (but not NSN) facilitated processing for the most concrete concepts, consistent with claims that linguistic information is more relevant for abstract concepts in early processing. Additionally, converging evidence from two datasets suggests that when NSN and CD are controlled for, the features that most facilitate processing are those associated with a concept's physical characteristics and real-world contexts. These results suggest that rich linguistic contexts (many semantic neighbors) facilitate early activation of abstract concepts, whereas concrete concepts benefit more from rich physical contexts (many associated objects and locations). PMID:23205008

Processing changes when listening to foreign-accented speech

PubMed Central

Romero-Rivas, Carlos; Martin, Clara D.; Costa, Albert

2015-01-01

This study investigates the mechanisms responsible for fast changes in processing foreign-accented speech. Event Related brain Potentials (ERPs) were obtained while native speakers of Spanish listened to native and foreign-accented speakers of Spanish. We observed a less positive P200 component for foreign-accented speech relative to native speech comprehension. This suggests that the extraction of spectral information and other important acoustic features was hampered during foreign-accented speech comprehension. However, the amplitude of the N400 component for foreign-accented speech comprehension decreased across the experiment, suggesting the use of a higher level, lexical mechanism. Furthermore, during native speech comprehension, semantic violations in the critical words elicited an N400 effect followed by a late positivity. During foreign-accented speech comprehension, semantic violations only elicited an N400 effect. Overall, our results suggest that, despite a lack of improvement in phonetic discrimination, native listeners experience changes at lexical-semantic levels of processing after brief exposure to foreign-accented speech. Moreover, these results suggest that lexical access, semantic integration and linguistic re-analysis processes are permeable to external factors, such as the accent of the speaker. PMID:25859209

Harvesting Intelligence in Multimedia Social Tagging Systems

NASA Astrophysics Data System (ADS)

Giannakidou, Eirini; Kaklidou, Foteini; Chatzilari, Elisavet; Kompatsiaris, Ioannis; Vakali, Athena

As more people adopt tagging practices, social tagging systems tend to form rich knowledge repositories that enable the extraction of patterns reflecting the way content semantics is perceived by the web users. This is of particular importance, especially in the case of multimedia content, since the availability of such content in the web is very high and its efficient retrieval using textual annotations or content-based automatically extracted metadata still remains a challenge. It is argued that complementing multimedia analysis techniques with knowledge drawn from web social annotations may facilitate multimedia content management. This chapter focuses on analyzing tagging patterns and combining them with content feature extraction methods, generating, thus, intelligence from multimedia social tagging systems. Emphasis is placed on using all available "tracks" of knowledge, that is tag co-occurrence together with semantic relations among tags and low-level features of the content. Towards this direction, a survey on the theoretical background and the adopted practices for analysis of multimedia social content are presented. A case study from Flickr illustrates the efficiency of the proposed approach.

Syntactic/semantic techniques for feature description and character recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonzalez, R.C.

1983-01-01

The Pattern Analysis Branch, Mapping, Charting and Geodesy (MC/G) Division, of the Naval Ocean Research and Development Activity (NORDA) has been involved over the past several years in the development of algorithms and techniques for computer recognition of free-form handprinted symbols as they appear on the Defense Mapping Agency (DMA) maps and charts. NORDA has made significant contributions to the automation of MC/G through advancing the state of the art in such information extraction techniques. In particular, new concepts in character (symbol) skeletonization, rugged feature measurements, and expert system-oriented decision logic have allowed the development of a very high performancemore » Handprinted Symbol Recognition (HSR) system for identifying depth soundings from naval smooth sheets (accuracies greater than 99.5%). The study reported in this technical note is part of NORDA's continuing research and development in pattern and shape analysis as it applies to Navy and DMA ocean/environment problems. The issue addressed in this technical note deals with emerging areas of syntactic and semantic techniques in pattern recognition as they might apply to the free-form symbol problem.« less

Right fusiform response patterns reflect visual object identity rather than semantic similarity.

PubMed

Bruffaerts, Rose; Dupont, Patrick; De Grauwe, Sophie; Peeters, Ronald; De Deyne, Simon; Storms, Gerrit; Vandenberghe, Rik

2013-12-01

We previously reported the neuropsychological consequences of a lesion confined to the middle and posterior part of the right fusiform gyrus (case JA) causing a partial loss of knowledge of visual attributes of concrete entities in the absence of category-selectivity (animate versus inanimate). We interpreted this in the context of a two-step model that distinguishes structural description knowledge from associative-semantic processing and implicated the lesioned area in the former process. To test this hypothesis in the intact brain, multi-voxel pattern analysis was used in a series of event-related fMRI studies in a total of 46 healthy subjects. We predicted that activity patterns in this region would be determined by the identity of rather than the conceptual similarity between concrete entities. In a prior behavioral experiment features were generated for each entity by more than 1000 subjects. Based on a hierarchical clustering analysis the entities were organised into 3 semantic clusters (musical instruments, vehicles, tools). Entities were presented as words or pictures. With foveal presentation of pictures, cosine similarity between fMRI response patterns in right fusiform cortex appeared to reflect both the identity of and the semantic similarity between the entities. No such effects were found for words in this region. The effect of object identity was invariant for location, scaling, orientation axis and color (grayscale versus color). It also persisted for different exemplars referring to a same concrete entity. The apparent semantic similarity effect however was not invariant. This study provides further support for a neurobiological distinction between structural description knowledge and processing of semantic relationships and confirms the role of right mid-posterior fusiform cortex in the former process, in accordance with previous lesion evidence. © 2013.

Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

A Semantic Analysis Method for Scientific and Engineering Code

NASA Technical Reports Server (NTRS)

Stewart, Mark E. M.

1998-01-01

This paper develops a procedure to statically analyze aspects of the meaning or semantics of scientific and engineering code. The analysis involves adding semantic declarations to a user's code and parsing this semantic knowledge with the original code using multiple expert parsers. These semantic parsers are designed to recognize formulae in different disciplines including physical and mathematical formulae and geometrical position in a numerical scheme. In practice, a user would submit code with semantic declarations of primitive variables to the analysis procedure, and its semantic parsers would automatically recognize and document some static, semantic concepts and locate some program semantic errors. A prototype implementation of this analysis procedure is demonstrated. Further, the relationship between the fundamental algebraic manipulations of equations and the parsing of expressions is explained. This ability to locate some semantic errors and document semantic concepts in scientific and engineering code should reduce the time, risk, and effort of developing and using these codes.

Acquiring concepts and features of novel words by two types of learning: direct mapping and inference.

PubMed

Chen, Shuang; Wang, Lin; Yang, Yufang

2014-04-01

This study examined the semantic representation of novel words learnt in two conditions: directly mapping a novel word to a concept (Direct mapping: DM) and inferring the concept from provided features (Inferred learning: IF). A condition where no definite concept could be inferred (No basic-level meaning: NM) served as a baseline. The semantic representation of the novel word was assessed via a semantic-relatedness judgment task. In this task, the learned novel word served as a prime, while the corresponding concept, an unlearned feature of the concept, and an unrelated word served as targets. ERP responses to the targets, primed by the novel words in the three learning conditions, were compared. For the corresponding concept, smaller N400s were elicited in the DM and IF conditions than in the NM condition, indicating that the concept could be obtained in both learning conditions. However, for the unlearned feature, the targets in the IF condition produced an N400 effect while in the DM condition elicited an LPC effect relative to the NM learning condition. No ERP difference was observed among the three learning conditions for the unrelated words. The results indicate that conditions of learning affect the semantic representation of novel word, and that the unlearned feature was only activated by the novel word in the IF learning condition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Semantic and topological classification of images in magnetically guided capsule endoscopy

NASA Astrophysics Data System (ADS)

Mewes, P. W.; Rennert, P.; Juloski, A. L.; Lalande, A.; Angelopoulou, E.; Kuth, R.; Hornegger, J.

2012-03-01

Magnetically-guided capsule endoscopy (MGCE) is a nascent technology with the goal to allow the steering of a capsule endoscope inside a water filled stomach through an external magnetic field. We developed a classification cascade for MGCE images with groups images in semantic and topological categories. Results can be used in a post-procedure review or as a starting point for algorithms classifying pathologies. The first semantic classification step discards over-/under-exposed images as well as images with a large amount of debris. The second topological classification step groups images with respect to their position in the upper gastrointestinal tract (mouth, esophagus, stomach, duodenum). In the third stage two parallel classifications steps distinguish topologically different regions inside the stomach (cardia, fundus, pylorus, antrum, peristaltic view). For image classification, global image features and local texture features were applied and their performance was evaluated. We show that the third classification step can be improved by a bubble and debris segmentation because it limits feature extraction to discriminative areas only. We also investigated the impact of segmenting intestinal folds on the identification of different semantic camera positions. The results of classifications with a support-vector-machine show the significance of color histogram features for the classification of corrupted images (97%). Features extracted from intestinal fold segmentation lead only to a minor improvement (3%) in discriminating different camera positions.

Common and differential electrophysiological mechanisms underlying semantic object memory retrieval probed by features presented in different stimulus types.

PubMed

Chiang, Hsueh-Sheng; Eroh, Justin; Spence, Jeffrey S; Motes, Michael A; Maguire, Mandy J; Krawczyk, Daniel C; Brier, Matthew R; Hart, John; Kraut, Michael A

2016-08-01

How the brain combines the neural representations of features that comprise an object in order to activate a coherent object memory is poorly understood, especially when the features are presented in different modalities (visual vs. auditory) and domains (verbal vs. nonverbal). We examined this question using three versions of a modified Semantic Object Retrieval Test, where object memory was probed by a feature presented as a written word, a spoken word, or a picture, followed by a second feature always presented as a visual word. Participants indicated whether each feature pair elicited retrieval of the memory of a particular object. Sixteen subjects completed one of the three versions (N=48 in total) while their EEG were recorded simultaneously. We analyzed EEG data in four separate frequency bands (delta: 1-4Hz, theta: 4-7Hz; alpha: 8-12Hz; beta: 13-19Hz) using a multivariate data-driven approach. We found that alpha power time-locked to response was modulated by both cross-modality (visual vs. auditory) and cross-domain (verbal vs. nonverbal) probing of semantic object memory. In addition, retrieval trials showed greater changes in all frequency bands compared to non-retrieval trials across all stimulus types in both response-locked and stimulus-locked analyses, suggesting dissociable neural subcomponents involved in binding object features to retrieve a memory. We conclude that these findings support both modality/domain-dependent and modality/domain-independent mechanisms during semantic object memory retrieval. Copyright © 2016 Elsevier B.V. All rights reserved.

Body-part-specific representations of semantic noun categories.

PubMed

Carota, Francesca; Moseley, Rachel; Pulvermüller, Friedemann

2012-06-01

Word meaning processing in the brain involves ventrolateral temporal cortex, but a semantic contribution of the dorsal stream, especially frontocentral sensorimotor areas, has been controversial. We here examine brain activation during passive reading of object-related nouns from different semantic categories, notably animal, food, and tool words, matched for a range of psycholinguistic features. Results show ventral stream activation in temporal cortex along with category-specific activation patterns in both ventral and dorsal streams, including sensorimotor systems and adjacent pFC. Precentral activation reflected action-related semantic features of the word categories. Cortical regions implicated in mouth and face movements were sparked by food words, and hand area activation was seen for tool words, consistent with the actions implicated by the objects the words are used to speak about. Furthermore, tool words specifically activated the right cerebellum, and food words activated the left orbito-frontal and fusiform areas. We discuss our results in the context of category-specific semantic deficits in the processing of words and concepts, along with previous neuroimaging research, and conclude that specific dorsal and ventral areas in frontocentral and temporal cortex index visual and affective-emotional semantic attributes of object-related nouns and action-related affordances of their referent objects.

Priming production: Neural evidence for enhanced automatic semantic activity preceding language production in schizophrenia.

PubMed

Kuperberg, Gina R; Delaney-Busch, Nathaniel; Fanucci, Kristina; Blackford, Trevor

2018-01-01

Lexico-semantic disturbances are considered central to schizophrenia. Clinically, their clearest manifestation is in language production. However, most studies probing their underlying mechanisms have used comprehension or categorization tasks. Here, we probed automatic semantic activity prior to language production in schizophrenia using event-related potentials (ERPs). 19 people with schizophrenia and 16 demographically-matched healthy controls named target pictures that were very quickly preceded by masked prime words. To probe automatic semantic activity prior to production, we measured the N400 ERP component evoked by these targets. To determine the origin of any automatic semantic abnormalities, we manipulated the type of relationship between prime and target such that they overlapped in (a) their semantic features (semantically related, e.g. "cake" preceding a < picture of a pie >, (b) their initial phonemes (phonemically related, e.g. "stomach" preceding a < picture of a starfish >), or (c) both their semantic features and their orthographic/phonological word form (identity related, e.g. "socks" preceding a < picture of socks >). For each of these three types of relationship, the same targets were paired with unrelated prime words (counterbalanced across lists). We contrasted ERPs and naming times to each type of related target with its corresponding unrelated target. People with schizophrenia showed abnormal N400 modulation prior to naming identity related (versus unrelated) targets: whereas healthy control participants produced a smaller amplitude N400 to identity related than unrelated targets, patients showed the opposite pattern, producing a larger N400 to identity related than unrelated targets. This abnormality was specific to the identity related targets. Just like healthy control participants, people with schizophrenia produced a smaller N400 to semantically related than to unrelated targets, and showed no difference in the N400 evoked by phonemically related and unrelated targets. There were no differences between the two groups in the pattern of naming times across conditions. People with schizophrenia can show abnormal neural activity associated with automatic semantic processing prior to language production. The specificity of this abnormality to the identity related targets suggests that that, rather than arising from abnormalities of either semantic features or lexical form alone, it may stem from disruptions of mappings (connections) between the meaning of words and their form.

OntoPop: An Ontology Population System for the Semantic Web

NASA Astrophysics Data System (ADS)

Thongkrau, Theerayut; Lalitrojwong, Pattarachai

The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

Object activation in semantic memory from visual multimodal feature input.

PubMed

Kraut, Michael A; Kremen, Sarah; Moo, Lauren R; Segal, Jessica B; Calhoun, Vincent; Hart, John

2002-01-01

The human brain's representation of objects has been proposed to exist as a network of coactivated neural regions present in multiple cognitive systems. However, it is not known if there is a region specific to the process of activating an integrated object representation in semantic memory from multimodal feature stimuli (e.g., picture-word). A previous study using word-word feature pairs as stimulus input showed that the left thalamus is integrally involved in object activation (Kraut, Kremen, Segal, et al., this issue). In the present study, participants were presented picture-word pairs that are features of objects, with the task being to decide if together they "activated" an object not explicitly presented (e.g., picture of a candle and the word "icing" activate the internal representation of a "cake"). For picture-word pairs that combine to elicit an object, signal change was detected in the ventral temporo-occipital regions, pre-SMA, left primary somatomotor cortex, both caudate nuclei, and the dorsal thalami bilaterally. These findings suggest that the left thalamus is engaged for either picture or word stimuli, but the right thalamus appears to be involved when picture stimuli are also presented with words in semantic object activation tasks. The somatomotor signal changes are likely secondary to activation of the semantic object representations from multimodal visual stimuli.

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

PubMed

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

Feature-level sentiment analysis by using comparative domain corpora

NASA Astrophysics Data System (ADS)

Quan, Changqin; Ren, Fuji

2016-06-01

Feature-level sentiment analysis (SA) is able to provide more fine-grained SA on certain opinion targets and has a wider range of applications on E-business. This study proposes an approach based on comparative domain corpora for feature-level SA. The proposed approach makes use of word associations for domain-specific feature extraction. First, we assign a similarity score for each candidate feature to denote its similarity extent to a domain. Then we identify domain features based on their similarity scores on different comparative domain corpora. After that, dependency grammar and a general sentiment lexicon are applied to extract and expand feature-oriented opinion words. Lastly, the semantic orientation of a domain-specific feature is determined based on the feature-oriented opinion lexicons. In evaluation, we compare the proposed method with several state-of-the-art methods (including unsupervised and semi-supervised) using a standard product review test collection. The experimental results demonstrate the effectiveness of using comparative domain corpora.

Neuroanatomic organization of sound memory in humans.

PubMed

Kraut, Michael A; Pitcock, Jeffery A; Calhoun, Vince; Li, Juan; Freeman, Thomas; Hart, John

2006-11-01

The neural interface between sensory perception and memory is a central issue in neuroscience, particularly initial memory organization following perceptual analyses. We used functional magnetic resonance imaging to identify anatomic regions extracting initial auditory semantic memory information related to environmental sounds. Two distinct anatomic foci were detected in the right superior temporal gyrus when subjects identified sounds representing either animals or threatening items. Threatening animal stimuli elicited signal changes in both foci, suggesting a distributed neural representation. Our results demonstrate both category- and feature-specific responses to nonverbal sounds in early stages of extracting semantic memory information from these sounds. This organization allows for these category-feature detection nodes to extract early, semantic memory information for efficient processing of transient sound stimuli. Neural regions selective for threatening sounds are similar to those of nonhuman primates, demonstrating semantic memory organization for basic biological/survival primitives are present across species.

Visual imagery processing and knowledge of famous names in Alzheimer's disease and MCI.

PubMed

Borg, Céline; Thomas-Antérion, Catherine; Bogey, Soline; Davier, Karine; Laurent, Bernard

2010-09-01

The study of memory for famous people and visual imagery retrieval was investigated in patients in the early stages of Alzheimer's disease (AD) and in the prodromal stage of AD, so-called Mild Cognitive Impairment (MCI). Fifteen patients with AD (MMSE > or = 23), 15 patients with amnestic MCI (a-MCI) and 15 normal controls (NC) performed a famous names test designed to evaluate the semantic and distinctive physical features knowledge of famous persons. Results indicated that patients with AD and a-MCI generated significantly less physical features and semantic biographical knowledge about famous persons than did normal control participants. Additionally, significant differences were observed between a-MCI and AD patients in all tasks. The present findings confirm recent studies reporting semantic memory impairment in MCI. Moreover, the current findings show that mental imagery is lowered in a-MCI and AD and is likely related to the early semantic impairment.

Semantically induced distortions of visual awareness in a patient with Balint's syndrome.

PubMed

Soto, David; Humphreys, Glyn W

2009-02-01

We present data indicating that visual awareness for a basic perceptual feature (colour) can be influenced by the relation between the feature and the semantic properties of the stimulus. We examined semantic interference from the meaning of a colour word (''RED") on simple colour (ink related) detection responses in a patient with simultagnosia due to bilateral parietal lesions. We found that colour detection was influenced by the congruency between the meaning of the word and the relevant ink colour, with impaired performance when the word and the colour mismatched (on incongruent trials). This result held even when remote associations between meaning and colour were used (i.e. the word ''PEA" influenced detection of the ink colour red). The results are consistent with a late locus of conscious visual experience that is derived at post-semantic levels. The implications for the understanding of the role of parietal cortex in object binding and visual awareness are discussed.

Integrated Japanese Dependency Analysis Using a Dialog Context

NASA Astrophysics Data System (ADS)

Ikegaya, Yuki; Noguchi, Yasuhiro; Kogure, Satoru; Itoh, Toshihiko; Konishi, Tatsuhiro; Kondo, Makoto; Asoh, Hideki; Takagi, Akira; Itoh, Yukihiro

This paper describes how to perform syntactic parsing and semantic analysis in a dialog system. The paper especially deals with how to disambiguate potentially ambiguous sentences using the contextual information. Although syntactic parsing and semantic analysis are often studied independently of each other, correct parsing of a sentence often requires the semantic information on the input and/or the contextual information prior to the input. Accordingly, we merge syntactic parsing with semantic analysis, which enables syntactic parsing taking advantage of the semantic content of an input and its context. One of the biggest problems of semantic analysis is how to interpret dependency structures. We employ a framework for semantic representations that circumvents the problem. Within the framework, the meaning of any predicate is converted into a semantic representation which only permits a single type of predicate: an identifying predicate "aru". The semantic representations are expressed as sets of "attribute-value" pairs, and those semantic representations are stored in the context information. Our system disambiguates syntactic/semantic ambiguities of inputs referring to the attribute-value pairs in the context information. We have experimentally confirmed the effectiveness of our approach; specifically, the experiment confirmed high accuracy of parsing and correctness of generated semantic representations.

Formal Semantics and Implementation of BPMN 2.0 Inclusive Gateways

NASA Astrophysics Data System (ADS)

Christiansen, David Raymond; Carbone, Marco; Hildebrandt, Thomas

We present the first direct formalization of the semantics of inclusive gateways as described in the Business Process Modeling Notation (BPMN) 2.0 Beta 1 specification. The formal semantics is given for a minimal subset of BPMN 2.0 containing just the inclusive and exclusive gateways and the start and stop events. By focusing on this subset we achieve a simple graph model that highlights the particular non-local features of the inclusive gateway semantics. We sketch two ways of implementing the semantics using algorithms based on incrementally updated data structures and also discuss distributed communication-based implementations of the two algorithms.

A meta-analysis of fMRI studies on Chinese orthographic, phonological, and semantic processing.

PubMed

Wu, Chiao-Yi; Ho, Moon-Ho Ringo; Chen, Shen-Hsing Annabel

2012-10-15

A growing body of neuroimaging evidence has shown that Chinese character processing recruits differential activation from alphabetic languages due to its unique linguistic features. As more investigations on Chinese character processing have recently become available, we applied a meta-analytic approach to summarize previous findings and examined the neural networks for orthographic, phonological, and semantic processing of Chinese characters independently. The activation likelihood estimation (ALE) method was used to analyze eight studies in the orthographic task category, eleven in the phonological and fifteen in the semantic task categories. Converging activation among three language-processing components was found in the left middle frontal gyrus, the left superior parietal lobule and the left mid-fusiform gyrus, suggesting a common sub-network underlying the character recognition process regardless of the task nature. With increasing task demands, the left inferior parietal lobule and the right superior temporal gyrus were specialized for phonological processing, while the left middle temporal gyrus was involved in semantic processing. Functional dissociation was identified in the left inferior frontal gyrus, with the posterior dorsal part for phonological processing and the anterior ventral part for semantic processing. Moreover, bilateral involvement of the ventral occipito-temporal regions was found for both phonological and semantic processing. The results provide better understanding of the neural networks underlying Chinese orthographic, phonological, and semantic processing, and consolidate the findings of additional recruitment of the left middle frontal gyrus and the right fusiform gyrus for Chinese character processing as compared with the universal language network that has been based on alphabetic languages. Copyright © 2012 Elsevier Inc. All rights reserved.

The semantic pathfinder: using an authoring metaphor for generic multimedia indexing.

PubMed

Snoek, Cees G M; Worring, Marcel; Geusebroek, Jan-Mark; Koelma, Dennis C; Seinstra, Frank J; Smeulders, Arnold W M

2006-10-01

This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives.

Memory as discrimination: what distraction reveals.

PubMed

Beaman, C Philip; Hanczakowski, Maciej; Hodgetts, Helen M; Marsh, John E; Jones, Dylan M

2013-11-01

Recalling information involves the process of discriminating between relevant and irrelevant information stored in memory. Not infrequently, the relevant information needs to be selected from among a series of related possibilities. This is likely to be particularly problematic when the irrelevant possibilities not only are temporally or contextually appropriate, but also overlap semantically with the target or targets. Here, we investigate the extent to which purely perceptual features that discriminate between irrelevant and target material can be used to overcome the negative impact of contextual and semantic relatedness. Adopting a distraction paradigm, it is demonstrated that when distractors are interleaved with targets presented either visually (Experiment 1) or auditorily (Experiment 2), a within-modality semantic distraction effect occurs; semantically related distractors impact upon recall more than do unrelated distractors. In the semantically related condition, the number of intrusions in recall is reduced, while the number of correctly recalled targets is simultaneously increased by the presence of perceptual cues to relevance (color features in Experiment 1 or speaker's gender in Experiment 2). However, as is demonstrated in Experiment 3, even presenting semantically related distractors in a language and a sensory modality (spoken Welsh) distinct from that of the targets (visual English) is insufficient to eliminate false recalls completely or to restore correct recall to levels seen with unrelated distractors . Together, the study shows how semantic and nonsemantic discriminability shape patterns of both erroneous and correct recall.

The Language of Entertainment News Is a Serious Business

ERIC Educational Resources Information Center

Marjanovic, Tatjana

2013-01-01

An essentially qualitative structural and semantic analysis is performed on the text of an "American Idol" coverage posted on yahoo.com January 24, 2013, constituting a micro-corpus of 2,739 words. Since such stories feature entertainment laced with a shot of drama and scandal, most of us share similar expectations as to what packaging…

Motor knowledge is one dimension for concept organization: further evidence from a Chinese semantic dementia case.

PubMed

Lin, Nan; Guo, Qihao; Han, Zaizhu; Bi, Yanchao

2011-11-01

Neuropsychological and neuroimaging studies have indicated that motor knowledge is one potential dimension along which concepts are organized. Here we present further direct evidence for the effects of motor knowledge in accounting for categorical patterns across object domains (living vs. nonliving) and grammatical domains (nouns vs. verbs), as well as the integrity of other modality-specific knowledge (e.g., visual). We present a Chinese case, XRK, who suffered from semantic dementia with left temporal lobe atrophy. In naming and comprehension tasks, he performed better at nonliving items than at living items, and better at verbs than at nouns. Critically, multiple regression method revealed that these two categorical effects could be both accounted for by the charade rating, a continuous measurement of the significance of motor knowledge for a concept or a semantic feature. Furthermore, charade rating also predicted his performances on the generation frequency of semantic features of various modalities. These findings consolidate the significance of motor knowledge in conceptual organization and further highlights the interactions between different types of semantic knowledge. Copyright © 2010 Elsevier Inc. All rights reserved.

[Study on the change of semantic perspective of schistosomiasis control in China].

PubMed

Zhou, Li-ying; Liu, Si-yuan; Li, Yu-ye; Deng, Yao; Yang, Kun

2015-12-01

To analyze the evolution process, discourse and semantic meaning of schistosomiasis prevention and control, so as to provide suggestions for control work. The official documents and mainstream media reports of schistosomiasis prevention and control were selected at different periods as discourse samples, and the deep social reasons behind the strategy change and the semantic meaning of the utterance were analyzed at different periods. The discourse of schistosomiasis prevention and control experienced the evolution of the political discourse, pluralistic discourse and public discourse, and the semantic connotations showed the authority conflict semantic features, and then transferred to semantic cooperation. The prevention and control of schistosomiasis have different semantic meanings at different periods, and the prevention and control work should correspond to a social practice, seek truth from facts, correctly understand the actual situation, and then establish the effective control policy.

Semantic richness effects in lexical decision: The role of feedback.

PubMed

Yap, Melvin J; Lim, Gail Y; Pexman, Penny M

2015-11-01

Across lexical processing tasks, it is well established that words with richer semantic representations are recognized faster. This suggests that the lexical system has access to meaning before a word is fully identified, and is consistent with a theoretical framework based on interactive and cascaded processing. Specifically, semantic richness effects are argued to be produced by feedback from semantic representations to lower-level representations. The present study explores the extent to which richness effects are mediated by feedback from lexical- to letter-level representations. In two lexical decision experiments, we examined the joint effects of stimulus quality and four semantic richness dimensions (imageability, number of features, semantic neighborhood density, semantic diversity). With the exception of semantic diversity, robust additive effects of stimulus quality and richness were observed for the targeted dimensions. Our results suggest that semantic feedback does not typically reach earlier levels of representation in lexical decision, and further reinforces the idea that task context modulates the processing dynamics of early word recognition processes.

Detecting modification of biomedical events using a deep parsing approach.

PubMed

Mackinlay, Andrew; Martinez, David; Baldwin, Timothy

2012-04-30

This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.

Conceptual Hierarchies in a Flat Attractor Network

PubMed Central

O’Connor, Christopher M.; Cree, George S.; McRae, Ken

2009-01-01

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. PMID:19543434

An ontology design pattern for surface water features

USGS Publications Warehouse

Sinha, Gaurav; Mark, David; Kolas, Dave; Varanka, Dalia; Romero, Boleslo E.; Feng, Chen-Chieh; Usery, E. Lynn; Liebermann, Joshua; Sorokine, Alexandre

2014-01-01

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

Transfer and Semantic Universals in the L2 Acquisition of the English Article System by Child L2 Learners

ERIC Educational Resources Information Center

Morales-Reyes, Alexandra; Soler, Inmaculada Gómez

2016-01-01

L2 learners' problems with English articles have been linked to learners' L1 and their access to universal semantic features (e.g., definiteness and specificity). Studies suggest that L2 adults rely on their L1 knowledge, while child L2 learners rely more on their access to semantic universals. The present study investigates whether child L2…

Comprehension of concrete and abstract words in semantic dementia

PubMed Central

Jefferies, Elizabeth; Patterson, Karalyn; Jones, Roy W.; Lambon Ralph, Matthew A.

2009-01-01

The vast majority of brain-injured patients with semantic impairment have better comprehension of concrete than abstract words. In contrast, several patients with semantic dementia (SD), who show circumscribed atrophy of the anterior temporal lobes bilaterally, have been reported to show reverse imageability effects, i.e., relative preservation of abstract knowledge. Although these reports largely concern individual patients, some researchers have recently proposed that superior comprehension of abstract concepts is a characteristic feature of SD. This would imply that the anterior temporal lobes are particularly crucial for processing sensory aspects of semantic knowledge, which are associated with concrete not abstract concepts. However, functional neuroimaging studies of healthy participants do not unequivocally predict reverse imageability effects in SD because the temporal poles sometimes show greater activation for more abstract concepts. We examined a case-series of eleven SD patients on a synonym judgement test that orthogonally varied the frequency and imageability of the items. All patients had higher success rates for more imageable as well as more frequent words, suggesting that (a) the anterior temporal lobes underpin semantic knowledge for both concrete and abstract concepts, (b) more imageable items – perhaps due to their richer multimodal representations – are typically more robust in the face of global semantic degradation and (c) reverse imageability effects are not a characteristic feature of SD. PMID:19586212

Lateralized semantic priming: modulation by levodopa, semantic distance, and participants’ magical beliefs

PubMed Central

Mohr, Christine; Landis, Theodor; Brugger, Peter

2006-01-01

We tested levodopa effects on lateralized direct and indirect semantic priming in 40 healthy right-handed men in a placebo-controlled, double-blind procedure. Crucially, priming was also analyzed as a function of participants’ positive schizotypal features (magical ideation, MI), previously found to be associated with an enhanced semantic spreading activation (SSA) within the right hemisphere. Across both priming conditions, we observed increased semantic priming in the levodopa group 1) specifically after right visual field stimulations and 2) in high MI scorers. In both instances, increased semantic priming emerged from exceedingly long reaction times to unrelated targets reflecting 1) the left hemisphere’s specialization for closely related concepts and 2) an opposite association between MI and SSA in the levodopa as compared with the placebo group. As a final finding, low MI scorers under levodopa performed like high MI scorers under placebo. Our findings speak against a general dopaminergic focusing of SSA, but one that respects each hemisphere’s specialization. They also suggest that individuals’ schizotypal features are important determinants of dopamine-induced changes in hemispheric functioning. We note that, in psychiatric patients, dopamine antagonists reportedly restore unusual lateralization. We discuss this dissociation between schizotypy and schizophrenia as supporting previous notions of protective brain mechanisms operating in the healthy “psychosis-prone” brain. PMID:19412448

Semantic web for integrated network analysis in biomedicine.

PubMed

Chen, Huajun; Ding, Li; Wu, Zhaohui; Yu, Tong; Dhanapalan, Lavanya; Chen, Jake Y

2009-03-01

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

The picture superiority effect in conceptual implicit memory: a conceptual distinctiveness hypothesis.

PubMed

Hamilton, Maryellen; Geraci, Lisa

2006-01-01

According to leading theories, the picture superiority effect is driven by conceptual processing, yet this effect has been difficult to obtain using conceptual implicit memory tests. We hypothesized that the picture superiority effect results from conceptual processing of a picture's distinctive features rather than a picture's semantic features. To test this hypothesis, we used 2 conceptual implicit general knowledge tests; one cued conceptually distinctive features (e.g., "What animal has large eyes?") and the other cued semantic features (e.g., "What animal is the figurehead of Tootsie Roll?"). Results showed a picture superiority effect only on the conceptual test using distinctive cues, supporting our hypothesis that this effect is mediated by conceptual processing of a picture's distinctive features.

Topic segmentation via community detection in complex networks

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Topic segmentation via community detection in complex networks.

PubMed

de Arruda, Henrique F; Costa, Luciano da F; Amancio, Diego R

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Automated classification of primary progressive aphasia subtypes from narrative speech transcripts.

PubMed

Fraser, Kathleen C; Meltzer, Jed A; Graham, Naida L; Leonard, Carol; Hirst, Graeme; Black, Sandra E; Rochon, Elizabeth

2014-06-01

In the early stages of neurodegenerative disorders, individuals may exhibit a decline in language abilities that is difficult to quantify with standardized tests. Careful analysis of connected speech can provide valuable information about a patient's language capacities. To date, this type of analysis has been limited by its time-consuming nature. In this study, we present a method for evaluating and classifying connected speech in primary progressive aphasia using computational techniques. Syntactic and semantic features were automatically extracted from transcriptions of narrative speech for three groups: semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. Features that varied significantly between the groups were used to train machine learning classifiers, which were then tested on held-out data. We achieved accuracies well above baseline on the three binary classification tasks. An analysis of the influential features showed that in contrast with controls, both patient groups tended to use words which were higher in frequency (especially nouns for SD, and verbs for PNFA). The SD patients also tended to use words (especially nouns) that were higher in familiarity, and they produced fewer nouns, but more demonstratives and adverbs, than controls. The speech of the PNFA group tended to be slower and incorporate shorter words than controls. The patient groups were distinguished from each other by the SD patients' relatively increased use of words which are high in frequency and/or familiarity. Copyright © 2012 Elsevier Ltd. All rights reserved.

The word processing deficit in semantic dementia: all categories are equal, but some categories are more equal than others.

PubMed

Pulvermüller, Friedemann; Cooper-Pye, Elisa; Dine, Clare; Hauk, Olaf; Nestor, Peter J; Patterson, Karalyn

2010-09-01

It has been claimed that semantic dementia (SD), the temporal variant of fronto-temporal dementia, is characterized by an across-the-board deficit affecting all types of conceptual knowledge. We here confirm this generalized deficit but also report differential degrees of impairment in processing specific semantic word categories in a case series of SD patients (N = 11). Within the domain of words with strong visually grounded meaning, the patients' lexical decision accuracy was more impaired for color-related than for form-related words. Likewise, within the domain of action verbs, the patients' performance was worse for words referring to face movements and speech acts than for words semantically linked to actions performed with the hand and arm. Psycholinguistic properties were matched between the stimulus groups entering these contrasts; an explanation for the differential degrees of impairment must therefore involve semantic features of the words in the different conditions. Furthermore, this specific pattern of deficits cannot be captured by classic category distinctions such as nouns versus verbs or living versus nonliving things. Evidence from previous neuroimaging research indicates that color- and face/speech-related words, respectively, draw most heavily on anterior-temporal and inferior-frontal areas, the structures most affected in SD. Our account combines (a) the notion of an anterior-temporal amodal semantic "hub" to explain the profound across-the-board deficit in SD word processing, with (b) a semantic topography model of category-specific circuits whose cortical distributions reflect semantic features of the words and concepts represented.

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

PubMed Central

Drew, Mark S.

2016-01-01

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction. PMID:28096807

Qualitative features of semantic fluency performance in mesial and lateral frontal patients.

PubMed

Reverberi, Carlo; Laiacona, Marcella; Capitani, Erminio

2006-01-01

Semantic verbal fluency is widely used in clinical and experimental studies. This task is highly sensitive to the presence of brain pathology and is frequently impaired after frontal lesions. Besides the total number of words generated, a qualitative analysis of their sequence can add valuable information about the impaired cognitive components. Thirty-four frontal patients and a group of matched controls were examined. Besides the number of words and subcategories retrieved by each group, we analysed two distinct aspects of the word sequence: the search strategy through a semantically organised store and the ability to switch from one subcategory to another. We checked whether the pattern of impairment changed according to the lesion site within the frontal lobe. Overall, patients produced fewer words than controls. However, only lateral frontal patients presented a reduced semantic relatedness between contiguously produced words and a specifically increased proportion of switches to different subcategories. The performance of lateral frontal patients was in line with the hypothesis of a search strategy impairment and cannot be attributed to a switching deficit. The performance of mesial frontal patients could be ascribed to a general deficit of activation.

The N400 reveals how personal semantics is processed: Insights into the nature and organization of self-knowledge

PubMed Central

Federmeier, Kara D.

2017-01-01

There is growing recognition that some important forms of long-term memory are difficult to classify into one of the well-studied memory subtypes. One example is personal semantics. Like the episodes that are stored as part of one’s autobiography, personal semantics is linked to an individual, yet, like general semantic memory, it is detached from a specific encoding context. Access to general semantics elicits an electrophysiological response known as the N400, which has been characterized across three decades of research; surprisingly, this response has not been fully examined in the context of personal semantics. In this study, we assessed responses to congruent and incongruent statements about people’s own, personal preferences. We found that access to personal preferences elicited N400 responses, with congruency effects that were similar in latency and distribution to those for general semantic statements elicited from the same participants. These results suggest that the processing of personal and general semantics share important functional and neurobiological features. PMID:26825011

A Corpus-Based Analysis of the Discourse Functions of "Ser/Estar" + Adjective in Three Levels of Spanish as FL Learners

ERIC Educational Resources Information Center

Collentine, Joe; Asencion-Delaney, Yuly

2010-01-01

Research on the acquisition of Spanish's two copulas, "ser" and "estar," provides an understanding of the interaction among syntax, semantics, pragmatics, morphology, and vocabulary during development (e.g., Geeslin, 2003a, 2003b; Gunterman, 1992; Ryan & Lafford, 1992). Recent research suggests that linguistic features in the surrounding discourse…

A top-down manner-based DCNN architecture for semantic image segmentation.

PubMed

Qiao, Kai; Chen, Jian; Wang, Linyuan; Zeng, Lei; Yan, Bin

2017-01-01

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.

Problems of teaching students to use the featured technologies in the area of semantic web

NASA Astrophysics Data System (ADS)

Klimov, V. V.; Chernyshov, A. A.; Balandina, A. I.; Kostkina, A. D.

2017-01-01

The following paper contains the description of up-to-date technologies in the area of web-services development, service-oriented architecture and the Semantic Web. The paper contains the analysis of the most popular and widespread technologies and methods in the semantic web area which are used in the developed educational course. In the paper, we also describe the problem of teaching students to use these technologies and specify conditions for the creation of the learning and development course. We also describe the main exercise for personal work and skills, which all the students learning this course have to gain. Moreover, in the paper we specify the problem with software which students are going to use while learning this course. In order to solve this problem, we introduce the developing system which will be used to support the laboratory works. For this moment this system supports only the fourth work execution, but our following plans contain the expansion of the system in order to support the leftover works.

Visual Information-Processing in the Perception of Features and Objects

DTIC Science & Technology

1989-01-05

or nodes in a semantic memory network, whereas recall and recognition depend on separate episodic memory traces. In our experiment, we used the same...problem for the account in terms of the separation of episodic from semantic memory , since no pre- existing representations of our line patterns were... semantic memory : amnesic patients were thought to have lost the ability to lay down (or retrieve) episodic traces of autobiographical events, but had

Acoustic structure of the five perceptual dimensions of timbre in orchestral instrument tones

PubMed Central

Elliott, Taffeta M.; Hamilton, Liberty S.; Theunissen, Frédéric E.

2013-01-01

Attempts to relate the perceptual dimensions of timbre to quantitative acoustical dimensions have been tenuous, leading to claims that timbre is an emergent property, if measurable at all. Here, a three-pronged analysis shows that the timbre space of sustained instrument tones occupies 5 dimensions and that a specific combination of acoustic properties uniquely determines gestalt perception of timbre. Firstly, multidimensional scaling (MDS) of dissimilarity judgments generated a perceptual timbre space in which 5 dimensions were cross-validated and selected by traditional model comparisons. Secondly, subjects rated tones on semantic scales. A discriminant function analysis (DFA) accounting for variance of these semantic ratings across instruments and between subjects also yielded 5 significant dimensions with similar stimulus ordination. The dimensions of timbre space were then interpreted semantically by rotational and reflectional projection of the MDS solution into two DFA dimensions. Thirdly, to relate this final space to acoustical structure, the perceptual MDS coordinates of each sound were regressed with its joint spectrotemporal modulation power spectrum. Sound structures correlated significantly with distances in perceptual timbre space. Contrary to previous studies, most perceptual timbre dimensions are not the result of purely temporal or spectral features but instead depend on signature spectrotemporal patterns. PMID:23297911

Objects and categories: feature statistics and object processing in the ventral stream.

PubMed

Tyler, Lorraine K; Chiu, Shannon; Zhuang, Jie; Randall, Billi; Devereux, Barry J; Wright, Paul; Clarke, Alex; Taylor, Kirsten I

2013-10-01

Recognizing an object involves more than just visual analyses; its meaning must also be decoded. Extensive research has shown that processing the visual properties of objects relies on a hierarchically organized stream in ventral occipitotemporal cortex, with increasingly more complex visual features being coded from posterior to anterior sites culminating in the perirhinal cortex (PRC) in the anteromedial temporal lobe (aMTL). The neurobiological principles of the conceptual analysis of objects remain more controversial. Much research has focused on two neural regions-the fusiform gyrus and aMTL, both of which show semantic category differences, but of different types. fMRI studies show category differentiation in the fusiform gyrus, based on clusters of semantically similar objects, whereas category-specific deficits, specifically for living things, are associated with damage to the aMTL. These category-specific deficits for living things have been attributed to problems in differentiating between highly similar objects, a process that involves the PRC. To determine whether the PRC and the fusiform gyri contribute to different aspects of an object's meaning, with differentiation between confusable objects in the PRC and categorization based on object similarity in the fusiform, we carried out an fMRI study of object processing based on a feature-based model that characterizes the degree of semantic similarity and difference between objects and object categories. Participants saw 388 objects for which feature statistic information was available and named the objects at the basic level while undergoing fMRI scanning. After controlling for the effects of visual information, we found that feature statistics that capture similarity between objects formed category clusters in fusiform gyri, such that objects with many shared features (typical of living things) were associated with activity in the lateral fusiform gyri whereas objects with fewer shared features (typical of nonliving things) were associated with activity in the medial fusiform gyri. Significantly, a feature statistic reflecting differentiation between highly similar objects, enabling object-specific representations, was associated with bilateral PRC activity. These results confirm that the statistical characteristics of conceptual object features are coded in the ventral stream, supporting a conceptual feature-based hierarchy, and integrating disparate findings of category responses in fusiform gyri and category deficits in aMTL into a unifying neurocognitive framework.

On the universal structure of human lexical semantics

PubMed Central

Sutton, Logan; Smith, Eric; Moore, Cristopher; Wilkins, Jon F.; Maddieson, Ian; Croft, William

2016-01-01

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word to express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. The methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use. PMID:26831113

Similarity of wh-Phrases and Acceptability Variation in wh-Islands

PubMed Central

Atkinson, Emily; Apple, Aaron; Rawlins, Kyle; Omaki, Akira

2016-01-01

In wh-questions that form a syntactic dependency between the fronted wh-phrase and its thematic position, acceptability is severely degraded when the dependency crosses another wh-phrase. It is well known that the acceptability degradation in wh-island violation ameliorates in certain contexts, but the source of this variation remains poorly understood. In the syntax literature, an influential theory – Featural Relativized Minimality – has argued that the wh-island effect is modulated exclusively by the distinctness of morpho-syntactic features in the two wh-phrases, but psycholinguistic theories of memory encoding and retrieval mechanisms predict that semantic properties of wh-phrases should also contribute to wh-island amelioration. We report four acceptability judgment experiments that systematically investigate the role of morpho-syntactic and semantic features in wh-island violations. The results indicate that the distribution of wh-island amelioration is best explained by an account that incorporates the distinctness of morpho-syntactic features as well as the semantic denotation of the wh-phrases. We argue that an integration of syntactic theories and perspectives from psycholinguistics can enrich our understanding of acceptability variation in wh-dependencies. PMID:26793156

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

A pool of pairs of related objects (POPORO) for investigating visual semantic integration: behavioral and electrophysiological validation.

PubMed

Kovalenko, Lyudmyla Y; Chaumon, Maximilien; Busch, Niko A

2012-07-01

Semantic processing of verbal and visual stimuli has been investigated in semantic violation or semantic priming paradigms in which a stimulus is either related or unrelated to a previously established semantic context. A hallmark of semantic priming is the N400 event-related potential (ERP)--a deflection of the ERP that is more negative for semantically unrelated target stimuli. The majority of studies investigating the N400 and semantic integration have used verbal material (words or sentences), and standardized stimulus sets with norms for semantic relatedness have been published for verbal but not for visual material. However, semantic processing of visual objects (as opposed to words) is an important issue in research on visual cognition. In this study, we present a set of 800 pairs of semantically related and unrelated visual objects. The images were rated for semantic relatedness by a sample of 132 participants. Furthermore, we analyzed low-level image properties and matched the two semantic categories according to these features. An ERP study confirmed the suitability of this image set for evoking a robust N400 effect of semantic integration. Additionally, using a general linear modeling approach of single-trial data, we also demonstrate that low-level visual image properties and semantic relatedness are in fact only minimally overlapping. The image set is available for download from the authors' website. We expect that the image set will facilitate studies investigating mechanisms of semantic and contextual processing of visual stimuli.

The Semantic Distance Task: Quantifying Semantic Distance with Semantic Network Path Length

ERIC Educational Resources Information Center

Kenett, Yoed N.; Levi, Effi; Anaki, David; Faust, Miriam

2017-01-01

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We…

The Function of Semantics in Automated Language Processing.

ERIC Educational Resources Information Center

Pacak, Milos; Pratt, Arnold W.

This paper is a survey of some of the major semantic models that have been developed for automated semantic analysis of natural language. Current approaches to semantic analysis and logical interference are based mainly on models of human cognitive processes such as Quillian's semantic memory, Simmon's Protosynthex III and others. All existing…

Legacy2Drupal - Conversion of an existing oceanographic relational database to a semantically enabled Drupal content management system

NASA Astrophysics Data System (ADS)

Maffei, A. R.; Chandler, C. L.; Work, T.; Allen, J.; Groman, R. C.; Fox, P. A.

2009-12-01

Content Management Systems (CMSs) provide powerful features that can be of use to oceanographic (and other geo-science) data managers. However, in many instances, geo-science data management offices have previously designed customized schemas for their metadata. The WHOI Ocean Informatics initiative and the NSF funded Biological Chemical and Biological Data Management Office (BCO-DMO) have jointly sponsored a project to port an existing, relational database containing oceanographic metadata, along with an existing interface coded in Cold Fusion middleware, to a Drupal6 Content Management System. The goal was to translate all the existing database tables, input forms, website reports, and other features present in the existing system to employ Drupal CMS features. The replacement features include Drupal content types, CCK node-reference fields, themes, RDB, SPARQL, workflow, and a number of other supporting modules. Strategic use of some Drupal6 CMS features enables three separate but complementary interfaces that provide access to oceanographic research metadata via the MySQL database: 1) a Drupal6-powered front-end; 2) a standard SQL port (used to provide a Mapserver interface to the metadata and data; and 3) a SPARQL port (feeding a new faceted search capability being developed). Future plans include the creation of science ontologies, by scientist/technologist teams, that will drive semantically-enabled faceted search capabilities planned for the site. Incorporation of semantic technologies included in the future Drupal 7 core release is also anticipated. Using a public domain CMS as opposed to proprietary middleware, and taking advantage of the many features of Drupal 6 that are designed to support semantically-enabled interfaces will help prepare the BCO-DMO database for interoperability with other ecosystem databases.

Affective priming effects of musical sounds on the processing of word meaning.

PubMed

Steinbeis, Nikolaus; Koelsch, Stefan

2011-03-01

Recent studies have shown that music is capable of conveying semantically meaningful concepts. Several questions have subsequently arisen particularly with regard to the precise mechanisms underlying the communication of musical meaning as well as the role of specific musical features. The present article reports three studies investigating the role of affect expressed by various musical features in priming subsequent word processing at the semantic level. By means of an affective priming paradigm, it was shown that both musically trained and untrained participants evaluated emotional words congruous to the affect expressed by a preceding chord faster than words incongruous to the preceding chord. This behavioral effect was accompanied by an N400, an ERP typically linked with semantic processing, which was specifically modulated by the (mis)match between the prime and the target. This finding was shown for the musical parameter of consonance/dissonance (Experiment 1) and then extended to mode (major/minor) (Experiment 2) and timbre (Experiment 3). Seeing that the N400 is taken to reflect the processing of meaning, the present findings suggest that the emotional expression of single musical features is understood by listeners as such and is probably processed on a level akin to other affective communications (i.e., prosody or vocalizations) because it interferes with subsequent semantic processing. There were no group differences, suggesting that musical expertise does not have an influence on the processing of emotional expression in music and its semantic connotations.

Integrating semantic dimension into openEHR archetypes for the management of cerebral palsy electronic medical records.

PubMed

Ellouze, Afef Samet; Bouaziz, Rafik; Ghorbel, Hanen

2016-10-01

Integrating semantic dimension into clinical archetypes is necessary once modeling medical records. First, it enables semantic interoperability and, it offers applying semantic activities on clinical data and provides a higher design quality of Electronic Medical Record (EMR) systems. However, to obtain these advantages, designers need to use archetypes that cover semantic features of clinical concepts involved in their specific applications. In fact, most of archetypes filed within open repositories are expressed in the Archetype Definition Language (ALD) which allows defining only the syntactic structure of clinical concepts weakening semantic activities on the EMR content in the semantic web environment. This paper focuses on the modeling of an EMR prototype for infants affected by Cerebral Palsy (CP), using the dual model approach and integrating semantic web technologies. Such a modeling provides a better delivery of quality of care and ensures semantic interoperability between all involved therapies' information systems. First, data to be documented are identified and collected from the involved therapies. Subsequently, data are analyzed and arranged into archetypes expressed in accordance of ADL. During this step, open archetype repositories are explored, in order to find the suitable archetypes. Then, ADL archetypes are transformed into archetypes expressed in OWL-DL (Ontology Web Language - Description Language). Finally, we construct an ontological source related to these archetypes enabling hence their annotation to facilitate data extraction and providing possibility to exercise semantic activities on such archetypes. Semantic dimension integration into EMR modeled in accordance to the archetype approach. The feasibility of our solution is shown through the development of a prototype, baptized "CP-SMS", which ensures semantic exploitation of CP EMR. This prototype provides the following features: (i) creation of CP EMR instances and their checking by using a knowledge base which we have constructed by interviews with domain experts, (ii) translation of initially CP ADL archetypes into CP OWL-DL archetypes, (iii) creation of an ontological source which we can use to annotate obtained archetypes and (vi) enrichment and supply of the ontological source and integration of semantic relations by providing hence fueling the ontology with new concepts, ensuring consistency and eliminating ambiguity between concepts. The degree of semantic interoperability that could be reached between EMR systems depends strongly on the quality of the used archetypes. Thus, the integration of semantic dimension in archetypes modeling process is crucial. By creating an ontological source and annotating archetypes, we create a supportive platform ensuring semantic interoperability between archetypes-based EMR-systems. Copyright © 2016. Published by Elsevier Inc.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

A memory learning framework for effective image retrieval.

PubMed

Han, Junwei; Ngan, King N; Li, Mingjing; Zhang, Hong-Jiang

2005-04-01

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

Influences of motor contexts on the semantic processing of action-related language.

PubMed

Yang, Jie

2014-09-01

The contribution of the sensory-motor system to the semantic processing of language stimuli is still controversial. To address the issue, the present article focuses on the impact of motor contexts (i.e., comprehenders' motor behaviors, motor-training experiences, and motor expertise) on the semantic processing of action-related language and reviews the relevant behavioral and neuroimaging findings. The existing evidence shows that although motor contexts can influence the semantic processing of action-related concepts, the mechanism of the contextual influences is still far from clear. Future investigations will be needed to clarify (1) whether motor contexts only modulate activity in motor regions, (2) whether the contextual influences are specific to the semantic features of language stimuli, and (3) what factors can determine the facilitatory or inhibitory contextual influences on the semantic processing of action-related language.

Detecting modification of biomedical events using a deep parsing approach

PubMed Central

2012-01-01

Background This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. Method To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. Results Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. Conclusions Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification. PMID:22595089

Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT

NASA Astrophysics Data System (ADS)

Madokoro, H.; Yamanashi, A.; Sato, K.

2013-08-01

This paper presents an unsupervised scene classification method for actualizing semantic recognition of indoor scenes. Background and foreground features are respectively extracted using Gist and color scale-invariant feature transform (SIFT) as feature representations based on context. We used hue, saturation, and value SIFT (HSV-SIFT) because of its simple algorithm with low calculation costs. Our method creates bags of features for voting visual words created from both feature descriptors to a two-dimensional histogram. Moreover, our method generates labels as candidates of categories for time-series images while maintaining stability and plasticity together. Automatic labeling of category maps can be realized using labels created using adaptive resonance theory (ART) as teaching signals for counter propagation networks (CPNs). We evaluated our method for semantic scene classification using KTH's image database for robot localization (KTH-IDOL), which is popularly used for robot localization and navigation. The mean classification accuracies of Gist, gray SIFT, one class support vector machines (OC-SVM), position-invariant robust features (PIRF), and our method are, respectively, 39.7, 58.0, 56.0, 63.6, and 79.4%. The result of our method is 15.8% higher than that of PIRF. Moreover, we applied our method for fine classification using our original mobile robot. We obtained mean classification accuracy of 83.2% for six zones.

What is in a contour map? A region-based logical formalization of contour semantics

USGS Publications Warehouse

Usery, E. Lynn; Hahmann, Torsten

2015-01-01

This paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.

SemVisM: semantic visualizer for medical image

NASA Astrophysics Data System (ADS)

Landaeta, Luis; La Cruz, Alexandra; Baranya, Alexander; Vidal, María.-Esther

2015-01-01

SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically, combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE.1

Actively learning human gaze shifting paths for semantics-aware photo cropping.

PubMed

Zhang, Luming; Gao, Yue; Ji, Rongrong; Xia, Yingjie; Dai, Qionghai; Li, Xuelong

2014-05-01

Photo cropping is a widely used tool in printing industry, photography, and cinematography. Conventional cropping models suffer from the following three challenges. First, the deemphasized role of semantic contents that are many times more important than low-level features in photo aesthetics. Second, the absence of a sequential ordering in the existing models. In contrast, humans look at semantically important regions sequentially when viewing a photo. Third, the difficulty of leveraging inputs from multiple users. Experience from multiple users is particularly critical in cropping as photo assessment is quite a subjective task. To address these challenges, this paper proposes semantics-aware photo cropping, which crops a photo by simulating the process of humans sequentially perceiving semantically important regions of a photo. We first project the local features (graphlets in this paper) onto the semantic space, which is constructed based on the category information of the training photos. An efficient learning algorithm is then derived to sequentially select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path, which simulates humans actively perceiving semantics in a photo. Furthermore, we learn a prior distribution of such active graphlet paths from training photos that are marked as aesthetically pleasing by multiple users. The learned priors enforce the corresponding active graphlet path of a test photo to be maximally similar to those from the training photos. Experimental results show that: 1) the active graphlet path accurately predicts human gaze shifting, and thus is more indicative for photo aesthetics than conventional saliency maps and 2) the cropped photos produced by our approach outperform its competitors in both qualitative and quantitative comparisons.

Feature precedence in processing multifeature visual information in the human brain: an event-related potential study.

PubMed

Liu, B; Meng, X; Wu, G; Huang, Y

2012-05-17

In this article, we aimed to study whether feature precedence existed in the cognitive processing of multifeature visual information in the human brain. In our experiment, we paid attention to two important visual features as follows: color and shape. In order to avoid the presence of semantic constraints between them and the resulting impact, pure color and simple geometric shape were chosen as the color feature and shape feature of visual stimulus, respectively. We adopted an "old/new" paradigm to study the cognitive processing of color feature, shape feature and the combination of color feature and shape feature, respectively. The experiment consisted of three tasks as follows: Color task, Shape task and Color-Shape task. The results showed that the feature-based pattern would be activated in the human brain in processing multifeature visual information without semantic association between features. Furthermore, shape feature was processed earlier than color feature, and the cognitive processing of color feature was more difficult than that of shape feature. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

The role of semantic self-perceptions in temporal distance perceptions toward autobiographical events: the semantic congruence model.

PubMed

Gebauer, Jochen E; Haddock, Geoffrey; Broemer, Philip; von Hecker, Ulrich

2013-11-01

Why do some autobiographical events feel as if they happened yesterday, whereas others feel like ancient history? Such temporal distance perceptions have surprisingly little to do with actual calendar time distance. Instead, psychologists have found that people typically perceive positive autobiographical events as overly recent, while perceiving negative events as overly distant. The origins of this temporal distance bias have been sought in self-enhancement strivings and mood congruence between autobiographical events and chronic mood. As such, past research exclusively focused on the evaluative features of autobiographical events, while neglecting semantic features. To close this gap, we introduce a semantic congruence model. Capitalizing on the Big Two self-perception dimensions, Study 1 showed that high semantic congruence between recalled autobiographical events and trait self-perceptions render the recalled events subjectively recent. Specifically, interpersonally warm (competent) individuals perceived autobiographical events reflecting warmth (competence) as relatively recent, but warm (competent) individuals did not perceive events reflecting competence (warmth) as relatively recent. Study 2 found that conscious perceptions of congruence mediate these effects. Studies 3 and 4 showed that neither mood congruence nor self-enhancement account for these results. Study 5 extended the results from the Big Two to the Big Five self-perception dimensions, while affirming the independence of the semantic congruence model from evaluative influences. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Semantic representation in the white matter pathway

PubMed Central

Fang, Yuxing; Wang, Xiaosha; Zhong, Suyu; Song, Luping; Han, Zaizhu; Gong, Gaolang

2018-01-01

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes. PMID:29624578

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

How to Modify (Implicit) Evaluations of Fear-Related Stimuli: Effects of Feature-Specific Attention Allocation

PubMed Central

Vanaelst, Jolien; Spruyt, Adriaan; De Houwer, Jan

2016-01-01

We demonstrate that feature-specific attention allocation influences the way in which repeated exposure modulates implicit and explicit evaluations toward fear-related stimuli. During an exposure procedure, participants were encouraged to assign selective attention either to the evaluative meaning (i.e., Evaluative Condition) or a non-evaluative, semantic feature (i.e., Semantic Condition) of fear-related stimuli. The influence of the exposure procedure was captured by means of a measure of implicit evaluation, explicit evaluative ratings, and a measure of automatic approach/avoidance tendencies. As predicted, the implicit measure of evaluation revealed a reduced expression of evaluations in the Semantic Condition as compared to the Evaluative Condition. Moreover, this effect generalized toward novel objects that were never presented during the exposure procedure. The explicit measure of evaluation mimicked this effect, although it failed to reach conventional levels of statistical significance. No effects were found in terms of automatic approach/avoidance tendencies. Potential implications for the treatment of anxiety disorders are discussed. PMID:27242626

How to Modify (Implicit) Evaluations of Fear-Related Stimuli: Effects of Feature-Specific Attention Allocation.

PubMed

Vanaelst, Jolien; Spruyt, Adriaan; De Houwer, Jan

2016-01-01

We demonstrate that feature-specific attention allocation influences the way in which repeated exposure modulates implicit and explicit evaluations toward fear-related stimuli. During an exposure procedure, participants were encouraged to assign selective attention either to the evaluative meaning (i.e., Evaluative Condition) or a non-evaluative, semantic feature (i.e., Semantic Condition) of fear-related stimuli. The influence of the exposure procedure was captured by means of a measure of implicit evaluation, explicit evaluative ratings, and a measure of automatic approach/avoidance tendencies. As predicted, the implicit measure of evaluation revealed a reduced expression of evaluations in the Semantic Condition as compared to the Evaluative Condition. Moreover, this effect generalized toward novel objects that were never presented during the exposure procedure. The explicit measure of evaluation mimicked this effect, although it failed to reach conventional levels of statistical significance. No effects were found in terms of automatic approach/avoidance tendencies. Potential implications for the treatment of anxiety disorders are discussed.

Ocean feature recognition using genetic algorithms with fuzzy fitness functions (GA/F3)

NASA Technical Reports Server (NTRS)

Ankenbrandt, C. A.; Buckles, B. P.; Petry, F. E.; Lybanon, M.

1990-01-01

A model for genetic algorithms with semantic nets is derived for which the relationships between concepts is depicted as a semantic net. An organism represents the manner in which objects in a scene are attached to concepts in the net. Predicates between object pairs are continuous valued truth functions in the form of an inverse exponential function (e sub beta lxl). 1:n relationships are combined via the fuzzy OR (Max (...)). Finally, predicates between pairs of concepts are resolved by taking the average of the combined predicate values of the objects attached to the concept at the tail of the arc representing the predicate in the semantic net. The method is illustrated by applying it to the identification of oceanic features in the North Atlantic.

Memory and emotion processing performance contributes to the diagnosis of non-semantic primary progressive aphasia syndromes.

PubMed

Piguet, Olivier; Leyton, Cristian E; Gleeson, Liam D; Hoon, Chris; Hodges, John R

2015-01-01

The two non-semantic variants of primary progressive aphasia (PPA), nonfluent/agrammatic PPA (nfv-PPA) and logopenic variant PPA (lv-PPA), share language features despite their different underlying pathology, and may be difficult to distinguish for non-language experts. To improve diagnostic accuracy of nfv-PPA and lv-PPA using tasks measuring non-language cognition and emotion processing. Thirty-eight dementia patients meeting diagnostic criteria for PPA (nfv-PPA 20, lv-PPA 18) and 21 matched healthy Controls underwent a comprehensive assessment of cognition and emotion processing, as well as a high-resolution structural MRI and a PiB-PET scan, a putative biomarker of Alzheimer's disease. Task performances were compared between the groups and those found to differ significantly were entered into a logistic regression analysis. Analyses revealed a double dissociation between nfv-PPA and lv-PPA. nfv-PPA exhibited significant emotion processing disturbance compared to lv-PPA and Controls. In contrast, only the lv-PPA group was significantly impaired on tasks of episodic memory. Logistic regression analyses showed that 87% of patients were correctly classified using emotion processing and episodic memory composite scores, together with a measure of visuospatial ability. Non-language presenting features can help differentiate between the two non-semantic PPA syndromes, with a double dissociation observed on tasks of episodic memory and emotion processing. Based on performance on these tasks, we propose a decision tree as a complementary method to differentiate between the two non-semantic variants. These findings have important clinical implications, with identification of patients who may potentially benefit existing therapeutic interventions currently available for Alzheimer's disease.

Semantic attributes are encoded in human electrocorticographic signals during visual object recognition.

PubMed

Rupp, Kyle; Roos, Matthew; Milsap, Griffin; Caceres, Carlos; Ratto, Christopher; Chevillet, Mark; Crone, Nathan E; Wolmetz, Michael

2017-03-01

Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole-brain functional Magnetic Resonance Imaging (fMRI), and we observed that high-gamma activity (70-110Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade-animate, canonically large-small, and places-tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

An Analysis of Differential Response Patterns on the Peabody Picture Vocabulary Test-IIIB in Struggling Adult Readers and Third-Grade Children

ERIC Educational Resources Information Center

Pae, Hye K.; Greenberg, Daphne; Williams, Rihana S.

2012-01-01

This study examines the Peabody Picture Vocabulary Test-IIIB (PPVT-IIIB) performance of 130 adults identified as struggling readers, in comparison to 175 third-grade children. Response patterns to the items on the PPVT-IIIB by these two groups were investigated, focusing on items, semantic categories, and lexical features, including word length,…

Mining Predictors of Success in Air Force Flight Training Regiments via Semantic Analysis of Instructor Evaluations

DTIC Science & Technology

2018-03-01

We apply our methodology to the criticism text written in the flight-training program student evaluations in order to construct a model that...factors. We apply our methodology to the criticism text written in the flight-training program student evaluations in order to construct a model...9 D. BINARY CLASSIFICATION AND FEATURE SELECTION ..........11 III. METHODOLOGY

How Visual and Semantic Information Influence Learning in Familiar Contexts

ERIC Educational Resources Information Center

Goujon, Annabelle; Brockmole, James R.; Ehinger, Krista A.

2012-01-01

Previous research using the contextual cuing paradigm has revealed both quantitative and qualitative differences in learning depending on whether repeated contexts are defined by letter arrays or real-world scenes. To clarify the relative contributions of visual features and semantic information likely to account for such differences, the typical…

Towards Text Copyright Detection Using Metadata in Web Applications

ERIC Educational Resources Information Center

Poulos, Marios; Korfiatis, Nikolaos; Bokos, George

2011-01-01

Purpose: This paper aims to present the semantic content identifier (SCI), a permanent identifier, computed through a linear-time onion-peeling algorithm that enables the extraction of semantic features from a text, and the integration of this information within the permanent identifier. Design/methodology/approach: The authors employ SCI to…

Neural Mechanisms of Conceptual Relations

ERIC Educational Resources Information Center

Lewis, Gwyneth A.

2017-01-01

An over-arching goal in neurolinguistic research is to characterize the neural bases of semantic representation. A particularly relevant goal concerns whether we represent features and events (a) together in a generalized semantic hub or (b) separately in distinct but complementary systems. While the left anterior temporal lobe (ATL) is strongly…

Differential Processing of Thematic and Categorical Conceptual Relations in Spoken Word Production

ERIC Educational Resources Information Center

de Zubicaray, Greig I.; Hansen, Samuel; McMahon, Katie L.

2013-01-01

Studies of semantic context effects in spoken word production have typically distinguished between categorical (or taxonomic) and associative relations. However, associates tend to confound semantic features or morphological representations, such as whole-part relations and compounds (e.g., BOAT-anchor, BEE-hive). Using a picture-word interference…

Semantic Preview Benefit during Reading

ERIC Educational Resources Information Center

Hohenstein, Sven; Kliegl, Reinhold

2014-01-01

Word features in parafoveal vision influence eye movements during reading. The question of whether readers extract semantic information from parafoveal words was studied in 3 experiments by using a gaze-contingent display change technique. Subjects read German sentences containing 1 of several preview words that were replaced by a target word…

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

On the Semantics of SPARQL

NASA Astrophysics Data System (ADS)

Arenas, Marcelo; Gutierrez, Claudio; Pérez, Jorge

The Resource Description Framework (RDF) is the standard data model for representing information about World Wide Web resources. In January 2008, it was released the recommendation of the W3C for querying RDF data, a query language called SPARQL. In this chapter, we give a detailed description of the semantics of this language. We start by focusing on the definition of a formal semantics for the core part of SPARQL, and then move to the definition for the entire language, including all the features in the specification of SPARQL by the W3C such as blank nodes in graph patterns and bag semantics for solutions.

Semantic memory in developmental amnesia.

PubMed

Elward, Rachael L; Vargha-Khadem, Faraneh

2018-04-30

Patients with developmental amnesia resulting from bilateral hippocampal atrophy associated with neonatal hypoxia-ischaemia typically show relatively preserved semantic memory and factual knowledge about the natural world despite severe impairments in episodic memory. Understanding the neural and mnemonic processes that enable this context-free semantic knowledge to be acquired throughout development without the support of the contextualised episodic memory system is a serious challenge. This review describes the clinical presentation of patients with developmental amnesia, contrasts its features with those reported for adult-onset hippocampal amnesia, and analyses the effects of variables that influence the learning of new semantic information. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

COTARD SYNDROME IN SEMANTIC DEMENTIA

PubMed Central

Mendez, Mario F.; Ramírez-Bermúdez, Jesús

2011-01-01

Background Semantic dementia is a neurodegenerative disorder characterized by the loss of meaning of words or concepts. semantic dementia can offer potential insights into the mechanisms of content-specific delusions. Objective The authors present a rare case of semantic dementia with Cotard syndrome, a delusion characterized by nihilism or self-negation. Method The semantic deficits and other features of semantic dementia were evaluated in relation to the patient's Cotard syndrome. Results Mrs. A developed the delusional belief that she was wasting and dying. This occurred after she lost knowledge for her somatic discomforts and sensations and for the organs that were the source of these sensations. Her nihilistic beliefs appeared to emerge from her misunderstanding of her somatic sensations. Conclusion This unique patient suggests that a mechanism for Cotard syndrome is difficulty interpreting the nature and source of internal pains and sensations. We propose that loss of semantic knowledge about one's own body may lead to the delusion of nihilism or death. PMID:22054629

Semi-Supervised Learning to Identify UMLS Semantic Relations.

PubMed

Luo, Yuan; Uzuner, Ozlem

2014-01-01

The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).

Designing learning management system interoperability in semantic web

NASA Astrophysics Data System (ADS)

Anistyasari, Y.; Sarno, R.; Rochmawati, N.

2018-01-01

The extensive adoption of learning management system (LMS) has set the focus on the interoperability requirement. Interoperability is the ability of different computer systems, applications or services to communicate, share and exchange data, information, and knowledge in a precise, effective and consistent way. Semantic web technology and the use of ontologies are able to provide the required computational semantics and interoperability for the automation of tasks in LMS. The purpose of this study is to design learning management system interoperability in the semantic web which currently has not been investigated deeply. Moodle is utilized to design the interoperability. Several database tables of Moodle are enhanced and some features are added. The semantic web interoperability is provided by exploited ontology in content materials. The ontology is further utilized as a searching tool to match user’s queries and available courses. It is concluded that LMS interoperability in Semantic Web is possible to be performed.

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

PubMed

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

The N400 reveals how personal semantics is processed: Insights into the nature and organization of self-knowledge.

PubMed

Coronel, Jason C; Federmeier, Kara D

2016-04-01

There is growing recognition that some important forms of long-term memory are difficult to classify into one of the well-studied memory subtypes. One example is personal semantics. Like the episodes that are stored as part of one's autobiography, personal semantics is linked to an individual, yet, like general semantic memory, it is detached from a specific encoding context. Access to general semantics elicits an electrophysiological response known as the N400, which has been characterized across three decades of research; surprisingly, this response has not been fully examined in the context of personal semantics. In this study, we assessed responses to congruent and incongruent statements about people's own, personal preferences. We found that access to personal preferences elicited N400 responses, with congruency effects that were similar in latency and distribution to those for general semantic statements elicited from the same participants. These results suggest that the processing of personal and general semantics share important functional and neurobiological features. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews.

PubMed

Ji, Xiaonan; Ritter, Alan; Yen, Po-Yin

2017-05-01

Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies. We developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure. We compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%. We demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability. Copyright © 2017 Elsevier Inc. All rights reserved.

A Robust Geometric Model for Argument Classification

NASA Astrophysics Data System (ADS)

Giannone, Cristina; Croce, Danilo; Basili, Roberto; de Cao, Diego

Argument classification is the task of assigning semantic roles to syntactic structures in natural language sentences. Supervised learning techniques for frame semantics have been recently shown to benefit from rich sets of syntactic features. However argument classification is also highly dependent on the semantics of the involved lexicals. Empirical studies have shown that domain dependence of lexical information causes large performance drops in outside domain tests. In this paper a distributional approach is proposed to improve the robustness of the learning model against out-of-domain lexical phenomena.

The Use of a Context-Based Information Retrieval Technique

DTIC Science & Technology

2009-07-01

provided in context. Latent Semantic Analysis (LSA) is a statistical technique for inferring contextual and structural information, and previous studies...WAIS). 10 DSTO-TR-2322 1.4.4 Latent Semantic Analysis LSA, which is also known as latent semantic indexing (LSI), uses a statistical and...1.4.6 Language Models In contrast, natural language models apply algorithms that combine statistical information with semantic information. Semantic

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Free-Form Region Description with Second-Order Pooling.

PubMed

Carreira, João; Caseiro, Rui; Batista, Jorge; Sminchisescu, Cristian

2015-06-01

Semantic segmentation and object detection are nowadays dominated by methods operating on regions obtained as a result of a bottom-up grouping process (segmentation) but use feature extractors developed for recognition on fixed-form (e.g. rectangular) patches, with full images as a special case. This is most likely suboptimal. In this paper we focus on feature extraction and description over free-form regions and study the relationship with their fixed-form counterparts. Our main contributions are novel pooling techniques that capture the second-order statistics of local descriptors inside such free-form regions. We introduce second-order generalizations of average and max-pooling that together with appropriate non-linearities, derived from the mathematical structure of their embedding space, lead to state-of-the-art recognition performance in semantic segmentation experiments without any type of local feature coding. In contrast, we show that codebook-based local feature coding is more important when feature extraction is constrained to operate over regions that include both foreground and large portions of the background, as typical in image classification settings, whereas for high-accuracy localization setups, second-order pooling over free-form regions produces results superior to those of the winning systems in the contemporary semantic segmentation challenges, with models that are much faster in both training and testing.

Coherent concepts are computed in the anterior temporal lobes.

PubMed

Lambon Ralph, Matthew A; Sage, Karen; Jones, Roy W; Mayberry, Emily J

2010-02-09

In his Philosophical Investigations, Wittgenstein famously noted that the formation of semantic representations requires more than a simple combination of verbal and nonverbal features to generate conceptually based similarities and differences. Classical and contemporary neuroscience has tended to focus upon how different neocortical regions contribute to conceptualization through the summation of modality-specific information. The additional yet critical step of computing coherent concepts has received little attention. Some computational models of semantic memory are able to generate such concepts by the addition of modality-invariant information coded in a multidimensional semantic space. By studying patients with semantic dementia, we demonstrate that this aspect of semantic memory becomes compromised following atrophy of the anterior temporal lobes and, as a result, the patients become increasingly influenced by superficial rather than conceptual similarities.

Influence of semantic consistency and perceptual features on visual attention during scene viewing in toddlers.

PubMed

Helo, Andrea; van Ommen, Sandrien; Pannasch, Sebastian; Danteny-Dordoigne, Lucile; Rämä, Pia

2017-11-01

Conceptual representations of everyday scenes are built in interaction with visual environment and these representations guide our visual attention. Perceptual features and object-scene semantic consistency have been found to attract our attention during scene exploration. The present study examined how visual attention in 24-month-old toddlers is attracted by semantic violations and how perceptual features (i. e. saliency, centre distance, clutter and object size) and linguistic properties (i. e. object label frequency and label length) affect gaze distribution. We compared eye movements of 24-month-old toddlers and adults while exploring everyday scenes which either contained an inconsistent (e.g., soap on a breakfast table) or consistent (e.g., soap in a bathroom) object. Perceptual features such as saliency, centre distance and clutter of the scene affected looking times in the toddler group during the whole viewing time whereas looking times in adults were affected only by centre distance during the early viewing time. Adults looked longer to inconsistent than consistent objects either if the objects had a high or a low saliency. In contrast, toddlers presented semantic consistency effect only when objects were highly salient. Additionally, toddlers with lower vocabulary skills looked longer to inconsistent objects while toddlers with higher vocabulary skills look equally long to both consistent and inconsistent objects. Our results indicate that 24-month-old children use scene context to guide visual attention when exploring the visual environment. However, perceptual features have a stronger influence in eye movement guidance in toddlers than in adults. Our results also indicate that language skills influence cognitive but not perceptual guidance of eye movements during scene perception in toddlers. Copyright © 2017 Elsevier Inc. All rights reserved.

Semantic data association for planar features in outdoor 6D-SLAM using lidar

NASA Astrophysics Data System (ADS)

Ulas, C.; Temeltas, H.

2013-05-01

Simultaneous Localization and Mapping (SLAM) is a fundamental problem of the autonomous systems in GPS (Global Navigation System) denied environments. The traditional probabilistic SLAM methods uses point features as landmarks and hold all the feature positions in their state vector in addition to the robot pose. The bottleneck of the point-feature based SLAM methods is the data association problem, which are mostly based on a statistical measure. The data association performance is very critical for a robust SLAM method since all the filtering strategies are applied after a known correspondence. For point-features, two different but very close landmarks in the same scene might be confused while giving the correspondence decision when their positions and error covariance matrix are solely taking into account. Instead of using the point features, planar features can be considered as an alternative landmark model in the SLAM problem to be able to provide a more consistent data association. Planes contain rich information for the solution of the data association problem and can be distinguished easily with respect to point features. In addition, planar maps are very compact since an environment has only very limited number of planar structures. The planar features does not have to be large structures like building wall or roofs; the small plane segments can also be used as landmarks like billboards, traffic posts and some part of the bridges in urban areas. In this paper, a probabilistic plane-feature extraction method from 3DLiDAR data and the data association based on the extracted semantic information of the planar features is introduced. The experimental results show that the semantic data association provides very satisfactory result in outdoor 6D-SLAM.

MCORES: a system for noun phrase coreference resolution for clinical records.

PubMed

Bodnari, Andreea; Szolovits, Peter; Uzuner, Özlem

2012-01-01

Narratives of electronic medical records contain information that can be useful for clinical practice and multi-purpose research. This information needs to be put into a structured form before it can be used by automated systems. Coreference resolution is a step in the transformation of narratives into a structured form. This study presents a medical coreference resolution system (MCORES) for noun phrases in four frequently used clinical semantic categories: persons, problems, treatments, and tests. MCORES treats coreference resolution as a binary classification task. Given a pair of concepts from a semantic category, it determines coreferent pairs and clusters them into chains. MCORES uses an enhanced set of lexical, syntactic, and semantic features. Some MCORES features measure the distance between various representations of the concepts in a pair and can be asymmetric. MCORES was compared with an in-house baseline that uses only single-perspective 'token overlap' and 'number agreement' features. MCORES was shown to outperform the baseline; its enhanced features contribute significantly to performance. In addition to the baseline, MCORES was compared against two available third-party, open-domain systems, RECONCILE(ACL09) and the Beautiful Anaphora Resolution Toolkit (BART). MCORES was shown to outperform both of these systems on clinical records.

On the universal structure of human lexical semantics

DOE PAGES

Youn, Hyejin; Sutton, Logan; Smith, Eric; ...

2016-02-01

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word tomore » express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. As a result, the methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use.« less

On the universal structure of human lexical semantics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Youn, Hyejin; Sutton, Logan; Smith, Eric

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word tomore » express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. As a result, the methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use.« less

SemanticSCo: A platform to support the semantic composition of services for gene expression analysis.

PubMed

Guardia, Gabriela D A; Ferreira Pires, Luís; da Silva, Eduardo G; de Farias, Cléver R G

2017-02-01

Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo. Copyright © 2017 Elsevier Inc. All rights reserved.

Representing nested semantic information in a linear string of text using XML.

PubMed

Krauthammer, Michael; Johnson, Stephen B; Hripcsak, George; Campbell, David A; Friedman, Carol

2002-01-01

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information.

Representing nested semantic information in a linear string of text using XML.

PubMed Central

Krauthammer, Michael; Johnson, Stephen B.; Hripcsak, George; Campbell, David A.; Friedman, Carol

2002-01-01

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information. PMID:12463856

Semantic Networks and Social Networks

ERIC Educational Resources Information Center

Downes, Stephen

2005-01-01

Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

Predicate Argument Structure Analysis for Use Case Description Modeling

NASA Astrophysics Data System (ADS)

Takeuchi, Hironori; Nakamura, Taiga; Yamaguchi, Takahira

In a large software system development project, many documents are prepared and updated frequently. In such a situation, support is needed for looking through these documents easily to identify inconsistencies and to maintain traceability. In this research, we focus on the requirements documents such as use cases and consider how to create models from the use case descriptions in unformatted text. In the model construction, we propose a few semantic constraints based on the features of the use cases and use them for a predicate argument structure analysis to assign semantic labels to actors and actions. With this approach, we show that we can assign semantic labels without enhancing any existing general lexical resources such as case frame dictionaries and design a less language-dependent model construction architecture. By using the constructed model, we consider a system for quality analysis of the use cases and automated test case generation to keep the traceability between document sets. We evaluated the reuse of the existing use cases and generated test case steps automatically with the proposed prototype system from real-world use cases in the development of a system using a packaged application. Based on the evaluation, we show how to construct models with high precision from English and Japanese use case data. Also, we could generate good test cases for about 90% of the real use cases through the manual improvement of the descriptions based on the feedback from the quality analysis system.

Semantic Social Network Portal for Collaborative Online Communities

ERIC Educational Resources Information Center

Neumann, Marco; O'Murchu, Ina; Breslin, John; Decker, Stefan; Hogan, Deirdre; MacDonaill, Ciaran

2005-01-01

Purpose: The motivation for this investigation is to apply social networking features to a semantic network portal, which supports the efforts in enterprise training units to up-skill the employee in the company, and facilitates the creation and reuse of knowledge in online communities. Design/methodology/approach: The paper provides an overview…

A Validation of Parafoveal Semantic Information Extraction in Reading Chinese

ERIC Educational Resources Information Center

Zhou, Wei; Kliegl, Reinhold; Yan, Ming

2013-01-01

Parafoveal semantic processing has recently been well documented in reading Chinese sentences, presumably because of language-specific features. However, because of a large variation of fixation landing positions on pretarget words, some preview words actually were located in foveal vision when readers' eyes landed close to the end of the…

Semantic Preview Benefit in Eye Movements during Reading: A Parafoveal Fast-Priming Study

ERIC Educational Resources Information Center

Hohenstein, Sven; Laubrock, Jochen; Kliegl, Reinhold

2010-01-01

Eye movements in reading are sensitive to foveal and parafoveal word features. Whereas the influence of orthographic or phonological parafoveal information on gaze control is undisputed, there has been no reliable evidence for early parafoveal extraction of semantic information in alphabetic script. Using a novel combination of the gaze-contingent…

Computer-Based Semantic Network in Molecular Biology: A Demonstration.

ERIC Educational Resources Information Center

Callman, Joshua L.; And Others

This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.

PubMed

Al-Saffar, Ahmed; Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-Bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

PubMed Central

Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach. PMID:29684036

Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts

PubMed Central

Fernández-Breis, Jesualdo Tomás; Maldonado, José Alberto; Marcos, Mar; Legaz-García, María del Carmen; Moner, David; Torres-Sospedra, Joaquín; Esteban-Gil, Angel; Martínez-Salvador, Begoña; Robles, Montserrat

2013-01-01

Background The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. Objective To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. Materials and methods We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. Results We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusions This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed. PMID:23934950

Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.

PubMed

Fernández-Breis, Jesualdo Tomás; Maldonado, José Alberto; Marcos, Mar; Legaz-García, María del Carmen; Moner, David; Torres-Sospedra, Joaquín; Esteban-Gil, Angel; Martínez-Salvador, Begoña; Robles, Montserrat

2013-12-01

The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed.

Semantic Feature Training in Combination with Transcranial Direct Current Stimulation (tDCS) for Progressive Anomia

PubMed Central

Hung, Jinyi; Bauer, Ashley; Grossman, Murray; Hamilton, Roy H.; Coslett, H. B.; Reilly, Jamie

2017-01-01

We examined the effectiveness of a 2-week regimen of a semantic feature training in combination with transcranial direct current stimulation (tDCS) for progressive naming impairment associated with primary progressive aphasia (N = 4) or early onset Alzheimer’s Disease (N = 1). Patients received a 2-week regimen (10 sessions) of anodal tDCS delivered over the left temporoparietal cortex while completing a language therapy that consisted of repeated naming and semantic feature generation. Therapy targets consisted of familiar people, household items, clothes, foods, places, hygiene implements, and activities. Untrained items from each semantic category provided item level controls. We analyzed naming accuracies at multiple timepoints (i.e., pre-, post-, 6-month follow-up) via a mixed effects logistic regression and individual differences in treatment responsiveness using a series of non-parametric McNemar tests. Patients showed advantages for naming trained over untrained items. These gains were evident immediately post tDCS. Trained items also showed a shallower rate of decline over 6-months relative to untrained items that showed continued progressive decline. Patients tolerated stimulation well, and sustained improvements in naming accuracy suggest that the current intervention approach is viable. Future implementation of a sham control condition will be crucial toward ascertaining whether neurostimulation and behavioral treatment act synergistically or alternatively whether treatment gains are exclusively attributable to either tDCS or the behavioral intervention. PMID:28559805

A Semantic Lexicon-Based Approach for Sense Disambiguation and Its WWW Application

NASA Astrophysics Data System (ADS)

di Lecce, Vincenzo; Calabrese, Marco; Soldo, Domenico

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts.

Verb deficits in Alzheimer’s disease and agrammatism: Implications for lexical organization☆

PubMed Central

Kim, Mikyong; Thompson, Cynthia K.

2011-01-01

This study examined the nature of verb deficits in 14 individuals with probable Alzheimer’s Disease (PrAD) and nine with agrammatic aphasia. Production was tested, controlling both semantic and syntactic features of verbs, using noun and verb naming, sentence completion, and narrative tasks. Noun and verb comprehension and a grammaticality judgment task also were administered. Results showed that while both PrAD and agrammatic subjects showed impaired verb naming, the syntactic features of verbs (i.e., argument structure) influenced agrammatic, but not Alzheimer’s disease patients’ verb production ability. That is, agrammatic patients showed progressively greater difficulty with verbs associated with more arguments, as has been shown in previous studies (e.g., Kim & Thompson, 2000; Thompson, 2003; Thompson, Lange, Schneider, & Shapiro, 1997), and suggest a syntactic basis for verb production deficits in agrammatism. Conversely, the semantic complexity of verbs affected PrAD, but not agrammatic, patients’ performance, suggesting “bottom-up” breakdown in their verb lexicon, paralleling that of nouns, resulting from the degradation or loss of semantic features of verbs. PMID:14698726

Multi-talker background and semantic priming effect

PubMed Central

Dekerle, Marie; Boulenger, Véronique; Hoen, Michel; Meunier, Fanny

2014-01-01

The reported studies have aimed to investigate whether informational masking in a multi-talker background relies on semantic interference between the background and target using an adapted semantic priming paradigm. In 3 experiments, participants were required to perform a lexical decision task on a target item embedded in backgrounds composed of 1–4 voices. These voices were Semantically Consistent (SC) voices (i.e., pronouncing words sharing semantic features with the target) or Semantically Inconsistent (SI) voices (i.e., pronouncing words semantically unrelated to each other and to the target). In the first experiment, backgrounds consisted of 1 or 2 SC voices. One and 2 SI voices were added in Experiments 2 and 3, respectively. The results showed a semantic priming effect only in the conditions where the number of SC voices was greater than the number of SI voices, suggesting that semantic priming depended on prime intelligibility and strategic processes. However, even if backgrounds were composed of 3 or 4 voices, reducing intelligibility, participants were able to recognize words from these backgrounds, although no semantic priming effect on the targets was observed. Overall this finding suggests that informational masking can occur at a semantic level if intelligibility is sufficient. Based on the Effortfulness Hypothesis, we also suggest that when there is an increased difficulty in extracting target signals (caused by a relatively high number of voices in the background), more cognitive resources were allocated to formal processes (i.e., acoustic and phonological), leading to a decrease in available resources for deeper semantic processing of background words, therefore preventing semantic priming from occurring. PMID:25400572

The role of the right hemisphere in semantic control: A case-series comparison of right and left hemisphere stroke

PubMed Central

Thompson, Hannah E.; Henshall, Lauren; Jefferies, Elizabeth

2016-01-01

Semantic control processes guide conceptual retrieval so that we are able to focus on non-dominant associations and features when these are required for the task or context, yet the neural basis of semantic control is not fully understood. Neuroimaging studies have emphasised the role of left inferior frontal gyrus (IFG) in controlled retrieval, while neuropsychological investigations of semantic control deficits have almost exclusively focussed on patients with left-sided damage (e.g., patients with semantic aphasia, SA). Nevertheless, activation in fMRI during demanding semantic tasks typically extends to right IFG. To investigate the role of the right hemisphere (RH) in semantic control, we compared nine RH stroke patients with 21 left-hemisphere SA patients, 11 mild SA cases and 12 healthy, aged-matched controls on semantic and executive tasks, plus experimental tasks that manipulated semantic control in paradigms particularly sensitive to RH damage. RH patients had executive deficits to parallel SA patients but they performed well on standard semantic tests. Nevertheless, multimodal semantic control deficits were found in experimental tasks involving facial emotions and the ‘summation’ of meaning across multiple items. On these tasks, RH patients showed effects similar to those in SA cases – multimodal deficits that were sensitive to distractor strength and cues and miscues, plus increasingly poor performance in cyclical matching tasks which repeatedly probed the same set of concepts. Thus, despite striking differences in single-item comprehension, evidence presented here suggests semantic control is bilateral, and disruption of this component of semantic cognition can be seen following damage to either hemisphere. PMID:26945505

Semantic, Lexical, and Phonological Influences on the Production of Verb Inflections in Agrammatic Aphasia

ERIC Educational Resources Information Center

Faroqi-Shah, Yasmeen; Thompson, Cynthia K.

2004-01-01

Verb inflection errors, often seen in agrammatic aphasic speech, have been attributed to either impaired encoding of diacritical features that specify tense and aspect, or to impaired affixation during phonological encoding. In this study we examined the effect of semantic markedness, word form frequency and affix frequency, as well as accuracy…

Brain Activation during Semantic Processing in Autism Spectrum Disorders via Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Harris, Gordon J.; Chabris, Christopher F.; Clark, Jill; Urban, Trinity; Aharon, Itzhak; Steele, Shelley; McGrath, Lauren; Condouris, Karen; Tager-Flusberg, Helen

2006-01-01

Language and communication deficits are core features of autism spectrum disorders (ASD), even in high-functioning adults with ASD. This study investigated brain activation patterns using functional magnetic resonance imaging in right-handed adult males with ASD and a control group, matched on age, handedness, and verbal IQ. Semantic processing in…

Semantic Markup for Literary Scholars: How Descriptive Markup Affects the Study and Teaching of Literature.

ERIC Educational Resources Information Center

Campbell, D. Grant

2002-01-01

Describes a qualitative study which investigated the attitudes of literary scholars towards the features of semantic markup for primary texts in XML format. Suggests that layout is a vital part of the reading process which implies that the standardization of DTDs (Document Type Definitions) should extend to styling as well. (Author/LRW)

Semantic Features, Perceptual Expectations, and Frequency as Factors in the Learning of Polar Spatial Adjective Concepts.

ERIC Educational Resources Information Center

Dunckley, Candida J. Lutes; Radtke, Robert C.

Two semantic theories of word learning, a perceptual complexity hypothesis (H. Clark, 1970) and a quantitative complexity hypothesis (E. Clark, 1972) were tested by teaching 24 preschoolers and 16 college students CVC labels for five polar spatial adjective concepts having single word representations in English, and for three having no direct…

Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

PubMed

Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

2014-01-01

Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

Blind image quality assessment via probabilistic latent semantic analysis.

PubMed

Yang, Xichen; Sun, Quansen; Wang, Tianshu

2016-01-01

We propose a blind image quality assessment that is highly unsupervised and training free. The new method is based on the hypothesis that the effect caused by distortion can be expressed by certain latent characteristics. Combined with probabilistic latent semantic analysis, the latent characteristics can be discovered by applying a topic model over a visual word dictionary. Four distortion-affected features are extracted to form the visual words in the dictionary: (1) the block-based local histogram; (2) the block-based local mean value; (3) the mean value of contrast within a block; (4) the variance of contrast within a block. Based on the dictionary, the latent topics in the images can be discovered. The discrepancy between the frequency of the topics in an unfamiliar image and a large number of pristine images is applied to measure the image quality. Experimental results for four open databases show that the newly proposed method correlates well with human subjective judgments of diversely distorted images.

Supervised pixel classification using a feature space derived from an artificial visual system

NASA Technical Reports Server (NTRS)

Baxter, Lisa C.; Coggins, James M.

1991-01-01

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Forced to remember: when memory is biased by salient information.

PubMed

Santangelo, Valerio

2015-04-15

The last decades have seen a rapid growing in the attempt to understand the key factors involved in the internal memory representation of the external world. Visual salience have been found to provide a major contribution in predicting the probability for an item/object embedded in a complex setting (i.e., a natural scene) to be encoded and then remembered later on. Here I review the existing literature highlighting the impact of perceptual- (based on low-level sensory features) and semantics-related salience (based on high-level knowledge) on short-term memory representation, along with the neural mechanisms underpinning the interplay between these factors. The available evidence reveal that both perceptual- and semantics-related factors affect attention selection mechanisms during the encoding of natural scenes. Biasing internal memory representation, both perceptual and semantics factors increase the probability to remember high- to the detriment of low-saliency items. The available evidence also highlight an interplay between these factors, with a reduced impact of perceptual-related salience in biasing memory representation as a function of the increasing availability of semantics-related salient information. The neural mechanisms underpinning this interplay involve the activation of different portions of the frontoparietal attention control network. Ventral regions support the assignment of selection/encoding priorities based on high-level semantics, while the involvement of dorsal regions reflects priorities assignment based on low-level sensory features. Copyright © 2015 Elsevier B.V. All rights reserved.

Use of Statechart Assertions for Modeling Human-in-the-Loop Security Analysis and Decision-Making Processes

DTIC Science & Technology

2012-06-01

THIS PAGE INTENTIONALLY LEFT BLANK xv LIST OF ACRONYMS AND ABBREVIATIONS BPM Business Process Model BPMN Business Process Modeling Notation C&A...checking leads to an improvement in the quality and success of enterprise software development. Business Process Modeling Notation ( BPMN ) is an...emerging standard that allows business processes to be captured in a standardized format. BPMN lacks formal semantics which leaves many of its features

Semantic modeling and structural synthesis of onboard electronics protection means as open information system

NASA Astrophysics Data System (ADS)

Zhevnerchuk, D. V.; Surkova, A. S.; Lomakina, L. S.; Golubev, A. S.

2018-05-01

The article describes the component representation approach and semantic models of on-board electronics protection from ionizing radiation of various nature. Semantic models are constructed, the feature of which is the representation of electronic elements, protection modules, sources of impact in the form of blocks with interfaces. The rules of logical inference and algorithms for synthesizing the object properties of the semantic network, imitating the interface between the components of the protection system and the sources of radiation, are developed. The results of the algorithm are considered using the example of radiation-resistant microcircuits 1645RU5U, 1645RT2U and the calculation and experimental method for estimating the durability of on-board electronics.

Neural differentiation of lexico-syntactic categories or semantic features? event-related potential evidence for both.

PubMed

Kellenbach, Marion L; Wijers, Albertus A; Hovius, Marjolijn; Mulder, Juul; Mulder, Gijsbertus

2002-05-15

Event-related potentials (ERPs) were used to investigate whether processing differences between nouns and verbs can be accounted for by the differential salience of visual-perceptual and motor attributes in their semantic specifications. Three subclasses of nouns and verbs were selected, which differed in their semantic attribute composition (abstract, high visual, high visual and motor). Single visual word presentation with a recognition memory task was used. While multiple robust and parallel ERP effects were observed for both grammatical class and attribute type, there were no interactions between these. This pattern of effects provides support for lexical-semantic knowledge being organized in a manner that takes account both of category-based (grammatical class) and attribute-based distinctions.

Ontology-based geospatial data query and integration

USGS Publications Warehouse

Zhao, T.; Zhang, C.; Wei, M.; Peng, Z.-R.

2008-01-01

Geospatial data sharing is an increasingly important subject as large amount of data is produced by a variety of sources, stored in incompatible formats, and accessible through different GIS applications. Past efforts to enable sharing have produced standardized data format such as GML and data access protocols such as Web Feature Service (WFS). While these standards help enabling client applications to gain access to heterogeneous data stored in different formats from diverse sources, the usability of the access is limited due to the lack of data semantics encoded in the WFS feature types. Past research has used ontology languages to describe the semantics of geospatial data but ontology-based queries cannot be applied directly to legacy data stored in databases or shapefiles, or to feature data in WFS services. This paper presents a method to enable ontology query on spatial data available from WFS services and on data stored in databases. We do not create ontology instances explicitly and thus avoid the problems of data replication. Instead, user queries are rewritten to WFS getFeature requests and SQL queries to database. The method also has the benefits of being able to utilize existing tools of databases, WFS, and GML while enabling query based on ontology semantics. ?? 2008 Springer-Verlag Berlin Heidelberg.

Fast mapping semantic features: performance of adults with normal language, history of disorders of spoken and written language, and attention deficit hyperactivity disorder on a word-learning task.

PubMed

Alt, Mary; Gutmann, Michelle L

2009-01-01

This study was designed to test the word learning abilities of adults with typical language abilities, those with a history of disorders of spoken or written language (hDSWL), and hDSWL plus attention deficit hyperactivity disorder (+ADHD). Sixty-eight adults were required to associate a novel object with a novel label, and then recognize semantic features of the object and phonological features of the label. Participants were tested for overt ability (accuracy) and covert processing (reaction time). The +ADHD group was less accurate at mapping semantic features and slower to respond to lexical labels than both other groups. Different factors correlated with word learning performance for each group. Adults with language and attention deficits are more impaired at word learning than adults with language deficits only. Despite behavioral profiles like typical peers, adults with hDSWL may use different processing strategies than their peers. Readers will be able to: (1) recognize the influence of a dual disability (hDSWL and ADHD) on word learning outcomes; (2) identify factors that may contribute to word learning in adults in terms of (a) the nature of the words to be learned and (b) the language processing of the learner.

Semantic knowledge fractionations: verbal propositions vs. perceptual input? Evidence from a child with Klinefelter syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2013-04-01

This paper addresses the relative independence of different types of lexical- and factually-based semantic knowledge in JM, a 9-year-old boy with Klinefelter syndrome (KS). JM was matched to typically developing (TD) controls on the basis of chronological age. Lexical-semantic knowledge was investigated for common noun (CN) and mathematical vocabulary items (MV). Factually-based semantic knowledge was investigated for general and number facts. For CN items, JM's lexical stores were of a normal size but the volume of correct 'sensory feature' semantic knowledge he generated within verbal item descriptions was significantly reduced. He was also significantly impaired at naming item descriptions and pictures, particularly for fruit and vegetables. There was also weak object decision for fruit and vegetables. In contrast, for MV items, JM's lexical stores were elevated, with no significant difference in the amount and type of correct semantic knowledge generated within verbal item descriptions and normal naming. JM's fact retrieval accuracy was normal for all types of factual knowledge. JM's performance indicated a dissociation between the representation of CN and MV vocabulary items during development. JM's preserved semantic knowledge of facts in the face of impaired semantic knowledge of vocabulary also suggests that factually-based semantic knowledge representation is not dependent on normal lexical-semantic knowledge during development. These findings are discussed in relation to the emergence of distinct semantic knowledge representations during development, due to differing degrees of dependency upon the acquisition and representation of semantic knowledge from verbal propositions and perceptual input.

Automatic Semantic Facilitation in Anterior Temporal Cortex Revealed through Multimodal Neuroimaging

PubMed Central

Gramfort, Alexandre; Hämäläinen, Matti S.; Kuperberg, Gina R.

2013-01-01

A core property of human semantic processing is the rapid, facilitatory influence of prior input on extracting the meaning of what comes next, even under conditions of minimal awareness. Previous work has shown a number of neurophysiological indices of this facilitation, but the mapping between time course and localization—critical for separating automatic semantic facilitation from other mechanisms—has thus far been unclear. In the current study, we used a multimodal imaging approach to isolate early, bottom-up effects of context on semantic memory, acquiring a combination of electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) measurements in the same individuals with a masked semantic priming paradigm. Across techniques, the results provide a strikingly convergent picture of early automatic semantic facilitation. Event-related potentials demonstrated early sensitivity to semantic association between 300 and 500 ms; MEG localized the differential neural response within this time window to the left anterior temporal cortex, and fMRI localized the effect more precisely to the left anterior superior temporal gyrus, a region previously implicated in semantic associative processing. However, fMRI diverged from early EEG/MEG measures in revealing semantic enhancement effects within frontal and parietal regions, perhaps reflecting downstream attempts to consciously access the semantic features of the masked prime. Together, these results provide strong evidence that automatic associative semantic facilitation is realized as reduced activity within the left anterior superior temporal cortex between 300 and 500 ms after a word is presented, and emphasize the importance of multimodal neuroimaging approaches in distinguishing the contributions of multiple regions to semantic processing. PMID:24155321

Modeling semantic aspects for cross-media image indexing.

PubMed

Monay, Florent; Gatica-Perez, Daniel

2007-10-01

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework.

The roles of shared vs. distinctive conceptual features in lexical access

PubMed Central

Vieth, Harrison E.; McMahon, Katie L.; de Zubicaray, Greig I.

2014-01-01

Contemporary models of spoken word production assume conceptual feature sharing determines the speed with which objects are named in categorically-related contexts. However, statistical models of concept representation have also identified a role for feature distinctiveness, i.e., features that identify a single concept and serve to distinguish it quickly from other similar concepts. In three experiments we investigated whether distinctive features might explain reports of counter-intuitive semantic facilitation effects in the picture word interference (PWI) paradigm. In Experiment 1, categorically-related distractors matched in terms of semantic similarity ratings (e.g., zebra and pony) and manipulated with respect to feature distinctiveness (e.g., a zebra has stripes unlike other equine species) elicited interference effects of comparable magnitude. Experiments 2 and 3 investigated the role of feature distinctiveness with respect to reports of facilitated naming with part-whole distractor-target relations (e.g., a hump is a distinguishing part of a CAMEL, whereas knee is not, vs. an unrelated part such as plug). Related part distractors did not influence target picture naming latencies significantly when the part denoted by the related distractor was not visible in the target picture (whether distinctive or not; Experiment 2). When the part denoted by the related distractor was visible in the target picture, non-distinctive part distractors slowed target naming significantly at SOA of −150 ms (Experiment 3). Thus, our results show that semantic interference does occur for part-whole distractor-target relations in PWI, but only when distractors denote features shared with the target and other category exemplars. We discuss the implications of these results for some recently developed, novel accounts of lexical access in spoken word production. PMID:25278914

The role of the right hemisphere in semantic control: A case-series comparison of right and left hemisphere stroke.

PubMed

Thompson, Hannah E; Henshall, Lauren; Jefferies, Elizabeth

2016-05-01

Semantic control processes guide conceptual retrieval so that we are able to focus on non-dominant associations and features when these are required for the task or context, yet the neural basis of semantic control is not fully understood. Neuroimaging studies have emphasised the role of left inferior frontal gyrus (IFG) in controlled retrieval, while neuropsychological investigations of semantic control deficits have almost exclusively focussed on patients with left-sided damage (e.g., patients with semantic aphasia, SA). Nevertheless, activation in fMRI during demanding semantic tasks typically extends to right IFG. To investigate the role of the right hemisphere (RH) in semantic control, we compared nine RH stroke patients with 21 left-hemisphere SA patients, 11 mild SA cases and 12 healthy, aged-matched controls on semantic and executive tasks, plus experimental tasks that manipulated semantic control in paradigms particularly sensitive to RH damage. RH patients had executive deficits to parallel SA patients but they performed well on standard semantic tests. Nevertheless, multimodal semantic control deficits were found in experimental tasks involving facial emotions and the 'summation' of meaning across multiple items. On these tasks, RH patients showed effects similar to those in SA cases - multimodal deficits that were sensitive to distractor strength and cues and miscues, plus increasingly poor performance in cyclical matching tasks which repeatedly probed the same set of concepts. Thus, despite striking differences in single-item comprehension, evidence presented here suggests semantic control is bilateral, and disruption of this component of semantic cognition can be seen following damage to either hemisphere. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The methodology of semantic analysis for extracting physical effects

NASA Astrophysics Data System (ADS)

Fomenkova, M. A.; Kamaev, V. A.; Korobkin, D. M.; Fomenkov, S. A.

2017-01-01

The paper represents new methodology of semantic analysis for physical effects extracting. This methodology is based on the Tuzov ontology that formally describes the Russian language. In this paper, semantic patterns were described to extract structural physical information in the form of physical effects. A new algorithm of text analysis was described.

Representation learning: a unified deep learning framework for automatic prostate MR segmentation.

PubMed

Liao, Shu; Gao, Yaozong; Oto, Aytekin; Shen, Dinggang

2013-01-01

Image representation plays an important role in medical image analysis. The key to the success of different medical image analysis algorithms is heavily dependent on how we represent the input data, namely features used to characterize the input image. In the literature, feature engineering remains as an active research topic, and many novel hand-crafted features are designed such as Haar wavelet, histogram of oriented gradient, and local binary patterns. However, such features are not designed with the guidance of the underlying dataset at hand. To this end, we argue that the most effective features should be designed in a learning based manner, namely representation learning, which can be adapted to different patient datasets at hand. In this paper, we introduce a deep learning framework to achieve this goal. Specifically, a stacked independent subspace analysis (ISA) network is adopted to learn the most effective features in a hierarchical and unsupervised manner. The learnt features are adapted to the dataset at hand and encode high level semantic anatomical information. The proposed method is evaluated on the application of automatic prostate MR segmentation. Experimental results show that significant segmentation accuracy improvement can be achieved by the proposed deep learning method compared to other state-of-the-art segmentation approaches.

Issues in Semantic Memory: A Response to Glass and Holyoak. Technical Report No. 101.

ERIC Educational Resources Information Center

Shoben, Edward J.; And Others

Glass and Holyoak (1975) have raised two issues related to the distinction between set-theoretic and network theories of semantic memory, contending that: (a) their version of a network theory, the Marker Search model, is conceptually and empirically superior to the Feature Comparison model version of a set-theoretic theory; and (b) the contrast…

The Food Code in the Yakut Culture: Semantics and Functions

ERIC Educational Resources Information Center

Gabysheva, Luiza Lvovna

2016-01-01

The relevance of researching the issue of a specific cultural meaning for a word in a folklore text is based on its being insufficiently studied and due to the importance for solving the problem of the folklore language semantic features. Yakut nominations for dairy products, which are the key words in the language of the Sakha people's folklore,…

Provision and Preservation of Knowledge: A Department of Educational and Psychological Research as Laboratory for Analyzing Scientific Discourse.

ERIC Educational Resources Information Center

Bierschenk, Bernhard

Two kinds of perspectives governing the provision and preservation of knowledge, a universal and an ecological perspective, are discussed in this paper. In the first case, scientific observations are represented through a semantic interpretation of facts. This is illustrated with a series of experiments on semantic feature perception in the recall…

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

PubMed

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2017-08-01

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours. Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

The nature-disorder paradox: A perceptual study on how nature is disorderly yet aesthetically preferred.

PubMed

Kotabe, Hiroki P; Kardan, Omid; Berman, Marc G

2017-08-01

Natural environments have powerful aesthetic appeal linked to their capacity for psychological restoration. In contrast, disorderly environments are aesthetically aversive, and have various detrimental psychological effects. But in our research, we have repeatedly found that natural environments are perceptually disorderly. What could explain this paradox? We present 3 competing hypotheses: the aesthetic preference for naturalness is more powerful than the aesthetic aversion to disorder (the nature-trumps-disorder hypothesis ); disorder is trivial to aesthetic preference in natural contexts (the harmless-disorder hypothesis ); and disorder is aesthetically preferred in natural contexts (the beneficial-disorder hypothesis ). Utilizing novel methods of perceptual study and diverse stimuli, we rule in the nature-trumps-disorder hypothesis and rule out the harmless-disorder and beneficial-disorder hypotheses. In examining perceptual mechanisms, we find evidence that high-level scene semantics are both necessary and sufficient for the nature-trumps-disorder effect. Necessity is evidenced by the effect disappearing in experiments utilizing only low-level visual stimuli (i.e., where scene semantics have been removed) and experiments utilizing a rapid-scene-presentation procedure that obscures scene semantics. Sufficiency is evidenced by the effect reappearing in experiments utilizing noun stimuli which remove low-level visual features. Furthermore, we present evidence that the interaction of scene semantics with low-level visual features amplifies the nature-trumps-disorder effect-the effect is weaker both when statistically adjusting for quantified low-level visual features and when using noun stimuli which remove low-level visual features. These results have implications for psychological theories bearing on the joint influence of low- and high-level perceptual inputs on affect and cognition, as well as for aesthetic design. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Localising semantic and syntactic processing in spoken and written language comprehension: an Activation Likelihood Estimation meta-analysis.

PubMed

Rodd, Jennifer M; Vitello, Sylvia; Woollams, Anna M; Adank, Patti

2015-02-01

We conducted an Activation Likelihood Estimation (ALE) meta-analysis to identify brain regions that are recruited by linguistic stimuli requiring relatively demanding semantic or syntactic processing. We included 54 functional MRI studies that explicitly varied the semantic or syntactic processing load, while holding constant demands on earlier stages of processing. We included studies that introduced a syntactic/semantic ambiguity or anomaly, used a priming manipulation that specifically reduced the load on semantic/syntactic processing, or varied the level of syntactic complexity. The results confirmed the critical role of the posterior left Inferior Frontal Gyrus (LIFG) in semantic and syntactic processing. These results challenge models of sentence comprehension highlighting the role of anterior LIFG for semantic processing. In addition, the results emphasise the posterior (but not anterior) temporal lobe for both semantic and syntactic processing. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse.

PubMed

Garrard, Peter; Rentoumi, Vassiliki; Gesierich, Benno; Miller, Bruce; Gorno-Tempini, Maria Luisa

2014-06-01

Advances in automatic text classification have been necessitated by the rapid increase in the availability of digital documents. Machine learning (ML) algorithms can 'learn' from data: for instance a ML system can be trained on a set of features derived from written texts belonging to known categories, and learn to distinguish between them. Such a trained system can then be used to classify unseen texts. In this paper, we explore the potential of the technique to classify transcribed speech samples along clinical dimensions, using vocabulary data alone. We report the accuracy with which two related ML algorithms [naive Bayes Gaussian (NBG) and naive Bayes multinomial (NBM)] categorized picture descriptions produced by: 32 semantic dementia (SD) patients versus 10 healthy, age-matched controls; and SD patients with left- (n = 21) versus right-predominant (n = 11) patterns of temporal lobe atrophy. We used information gain (IG) to identify the vocabulary features that were most informative to each of these two distinctions. In the SD versus control classification task, both algorithms achieved accuracies of greater than 90%. In the right- versus left-temporal lobe predominant classification, NBM achieved a high level of accuracy (88%), but this was achieved by both NBM and NBG when the features used in the training set were restricted to those with high values of IG. The most informative features for the patient versus control task were low frequency content words, generic terms and components of metanarrative statements. For the right versus left task the number of informative lexical features was too small to support any specific inferences. An enriched feature set, including values derived from Quantitative Production Analysis (QPA) may shed further light on this little understood distinction. Copyright © 2013 Elsevier Ltd. All rights reserved.

Conceptual Structure within and between Modalities

PubMed Central

Dilkina, Katia; Lambon Ralph, Matthew A.

2012-01-01

Current views of semantic memory share the assumption that conceptual representations are based on multimodal experience, which activates distinct modality-specific brain regions. This proposition is widely accepted, yet little is known about how each modality contributes to conceptual knowledge and how the structure of this contribution varies across these multiple information sources. We used verbal feature lists, features from drawings, and verbal co-occurrence statistics from latent semantic analysis to examine the informational structure in four domains of knowledge: perceptual, functional, encyclopedic, and verbal. The goals of the analysis were three-fold: (1) to assess the structure within individual modalities; (2) to compare structures between modalities; and (3) to assess the degree to which concepts organize categorically or randomly. Our results indicated significant and unique structure in all four modalities: perceptually, concepts organize based on prominent features such as shape, size, color, and parts; functionally, they group based on use and interaction; encyclopedically, they arrange based on commonality in location or behavior; and verbally, they group associatively or relationally. Visual/perceptual knowledge gives rise to the strongest hierarchical organization and is closest to classic taxonomic structure. Information is organized somewhat similarly in the perceptual and encyclopedic domains, which differs significantly from the structure in the functional and verbal domains. Notably, the verbal modality has the most unique organization, which is not at all categorical but also not random. The idiosyncrasy and complexity of conceptual structure across modalities raise the question of how all of these modality-specific experiences are fused together into coherent, multifaceted yet unified concepts. Accordingly, both methodological and theoretical implications of the present findings are discussed. PMID:23293593

Modulation of the N400 component in relation to hypomanic personality traits in a word meaning ambiguity resolution task.

PubMed

Raucher-Chéné, Delphine; Terrien, Sarah; Gobin, Pamela; Gierski, Fabien; Kaladjian, Arthur; Besche-Richard, Chrystel

2017-09-01

High levels of hypomanic personality traits have been associated with an increased risk of developing bipolar disorder (BD). Changes in semantic content, impaired verbal associations, abnormal prosody, and abnormal speed of language are core features of BD, and are thought to be related to semantic processing abnormalities. In the present study, we used event-related potentials to investigate the relation between semantic processing (N400 component) and hypomanic personality traits. We assessed 65 healthy young adults on the Hypomanic Personality Scale (HPS). Event-related potentials were recorded during a semantic ambiguity resolution task exploring semantic ambiguity (polysemous word ending a sentence) and congruency (target word semantically related to the sentence). As expected, semantic ambiguity and congruency both elicited an N400 effect across our sample. Correlation analyses showed a significant positive relationship between the Social Vitality subscore of the HPS and N400 modulation in the frontal region of interest in the incongruent unambiguous condition, and in the frontocentral region of interest in the incongruent ambiguous condition. We found differences in semantic processing (i.e., detection of incongruence and semantic inhibition) in individuals with higher Social Vitality subscores. In the light of the literature, we discuss the notion that a semantic processing impairment could be a potential marker of vulnerability to BD, and one that needs to be explored further in this clinical population. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.

Semantics by analogy for illustrative volume visualization☆

PubMed Central

Gerl, Moritz; Rautek, Peter; Isenberg, Tobias; Gröller, Eduard

2012-01-01

We present an interactive graphical approach for the explicit specification of semantics for volume visualization. This explicit and graphical specification of semantics for volumetric features allows us to visually assign meaning to both input and output parameters of the visualization mapping. This is in contrast to the implicit way of specifying semantics using transfer functions. In particular, we demonstrate how to realize a dynamic specification of semantics which allows to flexibly explore a wide range of mappings. Our approach is based on three concepts. First, we use semantic shader augmentation to automatically add rule-based rendering functionality to static visualization mappings in a shader program, while preserving the visual abstraction that the initial shader encodes. With this technique we extend recent developments that define a mapping between data attributes and visual attributes with rules, which are evaluated using fuzzy logic. Second, we let users define the semantics by analogy through brushing on renderings of the data attributes of interest. Third, the rules are specified graphically in an interface that provides visual clues for potential modifications. Together, the presented methods offer a high degree of freedom in the specification and exploration of rule-based mappings and avoid the limitations of a linguistic rule formulation. PMID:23576827

Identification of environmental sounds and melodies in syndromes of anterior temporal lobe degeneration.

PubMed

Golden, Hannah L; Downey, Laura E; Fletcher, Philip D; Mahoney, Colin J; Schott, Jonathan M; Mummery, Catherine J; Crutch, Sebastian J; Warren, Jason D

2015-05-15

Recognition of nonverbal sounds in semantic dementia and other syndromes of anterior temporal lobe degeneration may determine clinical symptoms and help to define phenotypic profiles. However, nonverbal auditory semantic function has not been widely studied in these syndromes. Here we investigated semantic processing in two key nonverbal auditory domains - environmental sounds and melodies - in patients with semantic dementia (SD group; n=9) and in patients with anterior temporal lobe atrophy presenting with behavioural decline (TL group; n=7, including four cases with MAPT mutations) in relation to healthy older controls (n=20). We assessed auditory semantic performance in each domain using novel, uniform within-modality neuropsychological procedures that determined sound identification based on semantic classification of sound pairs. Both the SD and TL groups showed comparable overall impairments of environmental sound and melody identification; individual patients generally showed superior identification of environmental sounds than melodies, however relative sparing of melody over environmental sound identification also occurred in both groups. Our findings suggest that nonverbal auditory semantic impairment is a common feature of neurodegenerative syndromes with anterior temporal lobe atrophy. However, the profile of auditory domain involvement varies substantially between individuals. Copyright © 2015. Published by Elsevier B.V.

Behavioural and magnetoencephalographic evidence for the interaction between semantic and episodic memory in healthy elderly subjects.

PubMed

La Corte, Valentina; Dalla Barba, Gianfranco; Lemaréchal, Jean-Didier; Garnero, Line; George, Nathalie

2012-10-01

The relationship between episodic and semantic memory systems has long been debated. Some authors argue that episodic memory is contingent on semantic memory (Tulving 1984), while others postulate that both systems are independent since they can be selectively damaged (Squire 1987). The interaction between these memory systems is particularly important in the elderly, since the dissociation of episodic and semantic memory defects characterize different aging-related pathologies. Here, we investigated the interaction between semantic knowledge and episodic memory processes associated with faces in elderly subjects using an experimental paradigm where the semantic encoding of famous and unknown faces was compared to their episodic recognition. Results showed that the level of semantic awareness of items affected the recognition of those items in the episodic memory task. Event-related magnetic fields confirmed this interaction between episodic and semantic memory: ERFs related to the old/new effect during the episodic task were markedly different for famous and unknown faces. The old/new effect for famous faces involved sustained activities maximal over right temporal sensors, showing a spatio-temporal pattern partly similar to that found for famous versus unknown faces during the semantic task. By contrast, an old/new effect for unknown faces was observed on left parieto-occipital sensors. These findings suggest that the episodic memory for famous faces activated the retrieval of stored semantic information, whereas it was based on items' perceptual features for unknown faces. Overall, our results show that semantic information interfered markedly with episodic memory processes and suggested that the neural substrates of these two memory systems overlap.

Neural correlates of lexical-semantic memory: A voxel-based morphometry study in mild AD, aMCI and normal aging

PubMed Central

Balthazar, Marcio L.F.; Yasuda, Clarissa L.; Lopes, Tátila M.; Pereira, Fabrício R.S.; Damasceno, Benito Pereira; Cendes, Fernando

2011-01-01

Neuroanatomical correlations of naming and lexical-semantic memory are not yet fully understood. The most influential approaches share the view that semantic representations reflect the manner in which information has been acquired through perception and action, and that each brain area processes different modalities of semantic representations. Despite these anatomical differences in semantic processing, generalization across different features that have similar semantic significance is one of the main characteristics of human cognition. Methods We evaluated the brain regions related to naming, and to the semantic generalization, of visually presented drawings of objects from the Boston Naming Test (BNT), which comprises different categories, such as animals, vegetables, tools, food, and furniture. In order to create a model of lesion method, a sample of 48 subjects presenting with a continuous decline both in cognitive functions, including naming skills, and in grey matter density (GMD) was compared to normal young adults with normal aging, amnestic mild cognitive impairment (aMCI) and mild Alzheimer’s disease (AD). Semantic errors on the BNT, as well as naming performance, were correlated with whole brain GMD as measured by voxel-based morphometry (VBM). Results The areas most strongly related to naming and to semantic errors were the medial temporal structures, thalami, superior and inferior temporal gyri, especially their anterior parts, as well as prefrontal cortices (inferior and superior frontal gyri). Conclusion The possible role of each of these areas in the lexical-semantic networks was discussed, along with their contribution to the models of semantic memory organization. PMID:29213726

A Knowledge-Based Representation Scheme for Environmental Science Models

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Dungan, Jennifer L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

The Role of Semantics in Next-Generation Online Virtual World-Based Retail Store

NASA Astrophysics Data System (ADS)

Sharma, Geetika; Anantaram, C.; Ghosh, Hiranmay

Online virtual environments are increasingly becoming popular for entrepreneurship. While interactions are primarily between avatars, some interactions could occur through intelligent chatbots. Such interactions require connecting to backend business applications to obtain information, carry out real-world transactions etc. In this paper, we focus on integrating business application systems with virtual worlds. We discuss the probable features of a next-generation online virtual world-based retail store and the technologies involved in realizing the features of such a store. In particular, we examine the role of semantics in integrating popular virtual worlds with business applications to provide natural language based interactions.

Computational approaches for predicting biomedical research collaborations.

PubMed

Zhang, Qing; Yu, Hong

2014-01-01

Biomedical research is increasingly collaborative, and successful collaborations often produce high impact work. Computational approaches can be developed for automatically predicting biomedical research collaborations. Previous works of collaboration prediction mainly explored the topological structures of research collaboration networks, leaving out rich semantic information from the publications themselves. In this paper, we propose supervised machine learning approaches to predict research collaborations in the biomedical field. We explored both the semantic features extracted from author research interest profile and the author network topological features. We found that the most informative semantic features for author collaborations are related to research interest, including similarity of out-citing citations, similarity of abstracts. Of the four supervised machine learning models (naïve Bayes, naïve Bayes multinomial, SVMs, and logistic regression), the best performing model is logistic regression with an ROC ranging from 0.766 to 0.980 on different datasets. To our knowledge we are the first to study in depth how research interest and productivities can be used for collaboration prediction. Our approach is computationally efficient, scalable and yet simple to implement. The datasets of this study are available at https://github.com/qingzhanggithub/medline-collaboration-datasets.

Cognitive search model and a new query paradigm

NASA Astrophysics Data System (ADS)

Xu, Zhonghui

2001-06-01

This paper proposes a cognitive model in which people begin to search pictures by using semantic content and find a right picture by judging whether its visual content is a proper visualization of the semantics desired. It is essential that human search is not just a process of matching computation on visual feature but rather a process of visualization of the semantic content known. For people to search electronic images in the way as they manually do in the model, we suggest that querying be a semantic-driven process like design. A query-by-design paradigm is prosed in the sense that what you design is what you find. Unlike query-by-example, query-by-design allows users to specify the semantic content through an iterative and incremental interaction process so that a retrieval can start with association and identification of the given semantic content and get refined while further visual cues are available. An experimental image retrieval system, Kuafu, has been under development using the query-by-design paradigm and an iconic language is adopted.

Supervised Outlier Detection in Large-Scale Mvs Point Clouds for 3d City Modeling Applications

NASA Astrophysics Data System (ADS)

Stucker, C.; Richard, A.; Wegner, J. D.; Schindler, K.

2018-05-01

We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images. What makes outlier removal hard are varying distributions of inliers and outliers across a scene. Heuristic outlier removal using a specific feature that encodes point distribution often delivers unsatisfying results. Although most outliers can be identified correctly (high recall), many inliers are erroneously removed (low precision), too. This aggravates object 3D reconstruction due to missing data. We thus propose to discriminatively learn class-specific distributions directly from the data to achieve high precision. We apply a standard Random Forest classifier that infers a binary label (inlier or outlier) for each 3D point in the raw, unfiltered point cloud and test two approaches for training. In the first, non-semantic approach, features are extracted without considering the semantic interpretation of the 3D points. The trained model approximates the average distribution of inliers and outliers across all semantic classes. Second, semantic interpretation is incorporated into the learning process, i.e. we train separate inlieroutlier classifiers per semantic class (building facades, roof, ground, vegetation, fields, and water). Performance of learned filtering is evaluated on several large SfM point clouds of cities. We find that results confirm our underlying assumption that discriminatively learning inlier-outlier distributions does improve precision over global heuristics by up to ≍ 12 percent points. Moreover, semantically informed filtering that models class-specific distributions further improves precision by up to ≍ 10 percent points, being able to remove very isolated building, roof, and water points while preserving inliers on building facades and vegetation.

ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed.

PubMed

Khalid, Zoya; Sezerman, Osman Ugur

2017-05-01

Extracting useful knowledge from an unstructured textual data is a challenging task for biologists, since biomedical literature is growing exponentially on a daily basis. Building an automated method for such tasks is gaining much attention of researchers. ZK DrugResist is an online tool that automatically extracts mutations and expression changes associated with drug resistance from PubMed. In this study we have extended our tool to include semantic relations extracted from biomedical text covering drug resistance and established a server including both of these features. Our system was tested for three relations, Resistance (R), Intermediate (I) and Susceptible (S) by applying hybrid feature set. From the last few decades the focus has changed to hybrid approaches as it provides better results. In our case this approach combines rule-based methods with machine learning techniques. The results showed 97.67% accuracy with 96% precision, recall and F-measure. The results have outperformed the previously existing relation extraction systems thus can facilitate computational analysis of drug resistance against complex diseases and further can be implemented on other areas of biomedicine. Copyright © 2017 Elsevier Inc. All rights reserved.

Managing biomedical image metadata for search and retrieval of similar images.

PubMed

Korenblum, Daniel; Rubin, Daniel; Napel, Sandy; Rodriguez, Cesar; Beaulieu, Chris

2011-08-01

Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

Must analysis of meaning follow analysis of form? A time course analysis

PubMed Central

Feldman, Laurie B.; Milin, Petar; Cho, Kit W.; Moscoso del Prado Martín, Fermín; O’Connor, Patrick A.

2015-01-01

Many models of word recognition assume that processing proceeds sequentially from analysis of form to analysis of meaning. In the context of morphological processing, this implies that morphemes are processed as units of form prior to any influence of their meanings. Some interpret the apparent absence of differences in recognition latencies to targets (SNEAK) in form and semantically similar (sneaky-SNEAK) and in form similar and semantically dissimilar (sneaker-SNEAK) prime contexts at a stimulus onset asynchrony (SOA) of 48 ms as consistent with this claim. To determine the time course over which degree of semantic similarity between morphologically structured primes and their targets influences recognition in the forward masked priming variant of the lexical decision paradigm, we compared facilitation for the same targets after semantically similar and dissimilar primes across a range of SOAs (34–100 ms). The effect of shared semantics on recognition latency increased linearly with SOA when long SOAs were intermixed (Experiments 1A and 1B) and latencies were significantly faster after semantically similar than dissimilar primes at homogeneous SOAs of 48 ms (Experiment 2) and 34 ms (Experiment 3). Results limit the scope of form-then-semantics models of recognition and demonstrate that semantics influences even the very early stages of recognition. Finally, once general performance across trials has been accounted for, we fail to provide evidence for individual differences in morphological processing that can be linked to measures of reading proficiency. PMID:25852512

Must analysis of meaning follow analysis of form? A time course analysis.

PubMed

Feldman, Laurie B; Milin, Petar; Cho, Kit W; Moscoso Del Prado Martín, Fermín; O'Connor, Patrick A

2015-01-01

Many models of word recognition assume that processing proceeds sequentially from analysis of form to analysis of meaning. In the context of morphological processing, this implies that morphemes are processed as units of form prior to any influence of their meanings. Some interpret the apparent absence of differences in recognition latencies to targets (SNEAK) in form and semantically similar (sneaky-SNEAK) and in form similar and semantically dissimilar (sneaker-SNEAK) prime contexts at a stimulus onset asynchrony (SOA) of 48 ms as consistent with this claim. To determine the time course over which degree of semantic similarity between morphologically structured primes and their targets influences recognition in the forward masked priming variant of the lexical decision paradigm, we compared facilitation for the same targets after semantically similar and dissimilar primes across a range of SOAs (34-100 ms). The effect of shared semantics on recognition latency increased linearly with SOA when long SOAs were intermixed (Experiments 1A and 1B) and latencies were significantly faster after semantically similar than dissimilar primes at homogeneous SOAs of 48 ms (Experiment 2) and 34 ms (Experiment 3). Results limit the scope of form-then-semantics models of recognition and demonstrate that semantics influences even the very early stages of recognition. Finally, once general performance across trials has been accounted for, we fail to provide evidence for individual differences in morphological processing that can be linked to measures of reading proficiency.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

PubMed

Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

2015-06-01

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

The contribution of executive control to semantic cognition: Convergent evidence from semantic aphasia and executive dysfunction.

PubMed

Thompson, Hannah E; Almaghyuli, Azizah; Noonan, Krist A; Barak, Ohr; Lambon Ralph, Matthew A; Jefferies, Elizabeth

2018-01-03

Semantic cognition, as described by the controlled semantic cognition (CSC) framework (Rogers et al., , Neuropsychologia, 76, 220), involves two key components: activation of coherent, generalizable concepts within a heteromodal 'hub' in combination with modality-specific features (spokes), and a constraining mechanism that manipulates and gates this knowledge to generate time- and task-appropriate behaviour. Executive-semantic goal representations, largely supported by executive regions such as frontal and parietal cortex, are thought to allow the generation of non-dominant aspects of knowledge when these are appropriate for the task or context. Semantic aphasia (SA) patients have executive-semantic deficits, and these are correlated with general executive impairment. If the CSC proposal is correct, patients with executive impairment should not only exhibit impaired semantic cognition, but should also show characteristics that align with those observed in SA. This possibility remains largely untested, as patients selected on the basis that they show executive impairment (i.e., with 'dysexecutive syndrome') have not been extensively tested on tasks tapping semantic control and have not been previously compared with SA cases. We explored conceptual processing in 12 patients showing symptoms consistent with dysexecutive syndrome (DYS) and 24 SA patients, using a range of multimodal semantic assessments which manipulated control demands. Patients with executive impairments, despite not being selected to show semantic impairments, nevertheless showed parallel patterns to SA cases. They showed strong effects of distractor strength, cues and miscues, and probe-target distance, plus minimal effects of word frequency on comprehension (unlike semantic dementia patients with degradation of conceptual knowledge). This supports a component process account of semantic cognition in which retrieval is shaped by control processes, and confirms that deficits in SA patients reflect difficulty controlling semantic retrieval. © 2018 The Authors. Journal of Neuropsychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions.

PubMed

Jackson, Rebecca L; Hoffman, Paul; Pobric, Gorana; Lambon Ralph, Matthew A

2016-02-03

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex. Copyright © 2016 Jackson et al.

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions

PubMed Central

Jackson, Rebecca L.; Hoffman, Paul; Pobric, Gorana

2016-01-01

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. SIGNIFICANCE STATEMENT Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex. PMID:26843633

High Performance Semantic Factoring of Giga-Scale Semantic Graph Databases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Adolf, Robert D.; Al-Saffar, Sinan

2010-10-04

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to bring high performance computational resources to bear on their analysis, interpretation, and visualization, especially with respect to their innate semantic structure. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multithreaded architecture of the Cray XMT platform, conventional clusters, and large data stores. In this paper we describe that architecture, and present the results of our deployingmore » that for the analysis of the Billion Triple dataset with respect to its semantic factors.« less

Computationally Efficient Clustering of Audio-Visual Meeting Data

NASA Astrophysics Data System (ADS)

Hung, Hayley; Friedland, Gerald; Yeo, Chuohao

This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the participants in a group discussion using the example of business meeting recordings. The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones. We first demonstrate computationally efficient algorithms that can identify who spoke and when, a problem in speech processing known as speaker diarization. We also extract visual activity features efficiently from MPEG4 video by taking advantage of the processing that was already done for video compression. Then, we present a method of associating the audio-visual data together so that the content of each participant can be managed individually. The methods presented in this article can be used as a principal component that enables many higher-level semantic analysis tasks needed in search, retrieval, and navigation.

Hierarchical structure for audio-video based semantic classification of sports video sequences

NASA Astrophysics Data System (ADS)

Kolekar, M. H.; Sengupta, S.

2005-07-01

A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

Conveying the concept of movement in music: An event-related brain potential study.

PubMed

Zhou, Linshu; Jiang, Cunmei; Wu, Yingying; Yang, Yufang

2015-10-01

This study on event-related brain potential investigated whether music can convey the concept of movement. Using a semantic priming paradigm, natural musical excerpts were presented to non-musicians, followed by semantically congruent or incongruent pictures that depicted objects either in motion or at rest. The priming effects were tested in object decision and implicit recognition tasks to distinguish the effects of automatic conceptual activation from response competition. Results showed that in both tasks, pictures that were incongruent to preceding musical excerpts elicited larger N400 than congruent pictures, suggesting that music can prime the representations of movement concepts. Results of the multiple regression analysis showed that movement expression could be well predicted by specific acoustic and musical features, indicating the associations between music per se and the processing of iconic musical meaning. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Methodology for the Development of RESTful Semantic Web Services for Gene Expression Analysis

PubMed Central

Guardia, Gabriela D. A.; Pires, Luís Ferreira; Vêncio, Ricardo Z. N.; Malmegrim, Kelen C. R.; de Farias, Cléver R. G.

2015-01-01

Gene expression studies are generally performed through multi-step analysis processes, which require the integrated use of a number of analysis tools. In order to facilitate tool/data integration, an increasing number of analysis tools have been developed as or adapted to semantic web services. In recent years, some approaches have been defined for the development and semantic annotation of web services created from legacy software tools, but these approaches still present many limitations. In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain. Therefore, this paper aims at defining an integrated methodology for the implementation of RESTful semantic web services created from gene expression analysis tools and the semantic annotation of such services. We have applied our methodology to the development of a number of services to support the analysis of different types of gene expression data, including microarray and RNASeq. All developed services are publicly available in the Gene Expression Analysis Services (GEAS) Repository at http://dcm.ffclrp.usp.br/lssb/geas. Additionally, we have used a number of the developed services to create different integrated analysis scenarios to reproduce parts of two gene expression studies documented in the literature. The first study involves the analysis of one-color microarray data obtained from multiple sclerosis patients and healthy donors. The second study comprises the analysis of RNA-Seq data obtained from melanoma cells to investigate the role of the remodeller BRG1 in the proliferation and morphology of these cells. Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services. Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis. PMID:26207740

A Methodology for the Development of RESTful Semantic Web Services for Gene Expression Analysis.

PubMed

Guardia, Gabriela D A; Pires, Luís Ferreira; Vêncio, Ricardo Z N; Malmegrim, Kelen C R; de Farias, Cléver R G

2015-01-01

Gene expression studies are generally performed through multi-step analysis processes, which require the integrated use of a number of analysis tools. In order to facilitate tool/data integration, an increasing number of analysis tools have been developed as or adapted to semantic web services. In recent years, some approaches have been defined for the development and semantic annotation of web services created from legacy software tools, but these approaches still present many limitations. In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain. Therefore, this paper aims at defining an integrated methodology for the implementation of RESTful semantic web services created from gene expression analysis tools and the semantic annotation of such services. We have applied our methodology to the development of a number of services to support the analysis of different types of gene expression data, including microarray and RNASeq. All developed services are publicly available in the Gene Expression Analysis Services (GEAS) Repository at http://dcm.ffclrp.usp.br/lssb/geas. Additionally, we have used a number of the developed services to create different integrated analysis scenarios to reproduce parts of two gene expression studies documented in the literature. The first study involves the analysis of one-color microarray data obtained from multiple sclerosis patients and healthy donors. The second study comprises the analysis of RNA-Seq data obtained from melanoma cells to investigate the role of the remodeller BRG1 in the proliferation and morphology of these cells. Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services. Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis.

User Requirements Analysis For Digital Library Application Using Quality Function Deployment.

NASA Astrophysics Data System (ADS)

Wulandari, Lily; Sularto, Lana; Yusnitasari, Tristyanti; Ikasari, Diana

2017-03-01

This study attemp to build Smart Digital Library to be used by the wider community wherever they are. The system is built in the form of Smart Digital Library portal which uses semantic similarity method (Semantic Similarity) to search journals, articles or books by title or author name. This method is also used to determine the recommended books to be read by visitors of Smart Digital Library based on testimony from a previous reader automatically. Steps being taken in the development of Smart Digital Library system is the analysis phase, design phase, testing and implementation phase. At this stage of the analysis using WebQual for the preparation of the instruments to be distributed to the respondents and the data obtained from the respondents will be processed using Quality Function Deployment. In the analysis phase has the purpose of identifying consumer needs and technical requirements. The analysis was performed to a digital library on the web digital library Gunadarma University, Bogor Institute of Agriculture, University of Indonesia, etc. The questionnaire was distributed to 200 respondents. The research methodology begins with the collection of user requirements and analyse it using QFD. Application design is funded by the government through a program of Featured Universities Research by the Directorate General of Higher Education (DIKTI). Conclusions from this research are identified which include the Consumer Requirements of digital library application. The elements of the consumers requirements consists of 13 elements and 25 elements of Engineering Characteristics digital library requirements. Therefore the design of digital library applications that will be built, is designed according to the findings by eliminating features that are not needed by restaurant based on QFD House of Quality.

False recall in the Deese–Roediger–McDermott paradigm: The roles of gist and associative strength

PubMed Central

Cann, David R.; McRae, Ken; Katz, Albert N.

2011-01-01

Theories of false memories, particularly in the Deese–Roediger–McDermott (DRM) paradigm, focus on word association strength and gist. Backward associative strength (BAS) is a strong predictor of false recall in this paradigm. However, other than being defined as a measure of association between studied list words and falsely recalled nonpresented critical words, there is little understanding of this variable. In Experiment 1, we used a knowledge-type taxonomy to classify the semantic relations in DRM stimuli. These knowledge types predicted false-recall probability, as well as BAS itself, with the most important being situation features, synonyms, and taxonomic relations. In three subsequent experiments, we demonstrated that lists composed solely of situation features can elicit a gist and produce false memories, particularly when monitoring processes are made more difficult. Our results identify the semantic factors that underlie BAS and suggest how considering semantic relations leads to a better understanding of gist formation. PMID:21506047

Degenerative jargon aphasia: unusual progression of logopenic/phonological progressive aphasia?

PubMed

Caffarra, Paolo; Gardini, Simona; Cappa, Stefano; Dieci, Francesca; Concari, Letizia; Barocco, Federica; Ghetti, Caterina; Ruffini, Livia; Prati, Guido Dalla Rosa

2013-01-01

Primary progressive aphasia (PPA) corresponds to the gradual degeneration of language which can occur as nonfluent/agrammatic PPA, semantic variant PPA or logopenic variant PPA. We describe the clinical evolution of a patient with PPA presenting jargon aphasia as a late feature. At the onset of the disease (ten years ago) the patient showed anomia and executive deficits, followed later on by phonemic paraphasias and neologisms, deficits in verbal short-term memory, naming, verbal and semantic fluency. At recent follow-up the patient developed an unintelligible jargon with both semantic and neologistic errors, as well as with severe deficit of comprehension which precluded any further neuropsychological assessment. Compared to healthy controls, FDG-PET showed a hypometabolism in the left angular and middle temporal gyri, precuneus, caudate, posterior cingulate, middle frontal gyrus, and bilaterally in the superior temporal and inferior frontal gyri. The clinical and neuroimaging profile seems to support the hypothesis that the patient developed a late feature of logopenic variant PPA characterized by jargonaphasia and associated with superior temporal and parietal dysfunction.

Interpreting Definiteness in a Second Language without Articles: The Case of L2 Russian

ERIC Educational Resources Information Center

Cho, Jacee; Slabakova, Roumyana

2014-01-01

This article investigates the second language (L2) acquisition of two expressions of the semantic feature [definite] in Russian, a language without articles, by English and Korean native speakers. Within the Feature Reassembly approach (Lardiere, 2009), Slabakova (2009) has argued that reassembling features that are represented overtly in the…

Feature Types and Object Categories: Is Sensorimotoric Knowledge Different for Living and Nonliving Things?

ERIC Educational Resources Information Center

Ankerstein, Carrie A.; Varley, Rosemary A.; Cowell, Patricia E.

2012-01-01

Some models of semantic memory claim that items from living and nonliving domains have different feature-type profiles. Data from feature generation and perceptual modality rating tasks were compared to evaluate this claim. Results from two living (animals, fruits/vegetables) and two nonliving (tools, vehicles) categories showed that…

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

ERIC Educational Resources Information Center

Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N.

2016-01-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…

Remote semantic memory is impoverished in hippocampal amnesia

PubMed Central

Klooster, Nathaniel B.; Duff, Melissa C.

2015-01-01

The necessity of the hippocampus for acquiring new semantic concepts is a topic of considerable debate. However, it is generally accepted that any role the hippocampus plays in semantic memory is time limited and that previously acquired information becomes independent of the hippocampus over time. This view, along with intact naming and word-definition matching performance in amnesia, has led to the notion that remote semantic memory is intact in patients with hippocampal amnesia. Motivated by perspectives of word learning as a protracted process where additional features and senses of a word are added over time, and by recent discoveries about the time course of hippocampal contributions to on-line relational processing, reconsolidation, and the flexible integration of information, we revisit the notion that remote semantic memory is intact in amnesia. Using measures of semantic richness and vocabulary depth from psycholinguistics and first and second language-learning studies, we examined how much information is associated with previously acquired, highly familiar words in a group of patients with bilateral hippocampal damage and amnesia. Relative to healthy demographically matched comparison participants and a group of brain-damaged comparison participants, the patients with hippocampal amnesia performed significantly worse on both productive and receptive measures of vocabulary depth and semantic richness. These findings suggest that remote semantic memory is impoverished in patients with hippocampal amnesia and that the hippocampus may play a role in the maintenance and updating of semantic memory beyond its initial acquisition. PMID:26474741

Remote semantic memory is impoverished in hippocampal amnesia.

PubMed

Klooster, Nathaniel B; Duff, Melissa C

2015-12-01

The necessity of the hippocampus for acquiring new semantic concepts is a topic of considerable debate. However, it is generally accepted that any role the hippocampus plays in semantic memory is time limited and that previously acquired information becomes independent of the hippocampus over time. This view, along with intact naming and word-definition matching performance in amnesia, has led to the notion that remote semantic memory is intact in patients with hippocampal amnesia. Motivated by perspectives of word learning as a protracted process where additional features and senses of a word are added over time, and by recent discoveries about the time course of hippocampal contributions to on-line relational processing, reconsolidation, and the flexible integration of information, we revisit the notion that remote semantic memory is intact in amnesia. Using measures of semantic richness and vocabulary depth from psycholinguistics and first and second language-learning studies, we examined how much information is associated with previously acquired, highly familiar words in a group of patients with bilateral hippocampal damage and amnesia. Relative to healthy demographically matched comparison participants and a group of brain-damaged comparison participants, the patients with hippocampal amnesia performed significantly worse on both productive and receptive measures of vocabulary depth and semantic richness. These findings suggest that remote semantic memory is impoverished in patients with hippocampal amnesia and that the hippocampus may play a role in the maintenance and updating of semantic memory beyond its initial acquisition. Copyright © 2015 Elsevier Ltd. All rights reserved.

Remembering episodic memories is not necessary for forgetting of negative words: Semantic retrieval can cause forgetting of negative words.

PubMed

Kobayashi, Masanori; Tanno, Yoshihiko

2015-06-01

Retrieval of a memory can induce forgetting of other related memories, which is known as retrieval-induced forgetting. Although most studies have investigated retrieval-induced forgetting by remembering episodic memories, this also can occur by remembering semantic memories. The present study shows that retrieval of semantic memories can lead to forgetting of negative words. In two experiments, participants learned words and then engaged in retrieval practice where they were asked to recall words related to the learned words from semantic memory. Finally, participants completed a stem-cued recall test for the learned words. The results showed forgetting of neutral and negative words, which was characteristic of semantic retrieval-induced forgetting. A certain degree of overlapping features, except same learning episode, is sufficient to cause retrieval-induced forgetting of negative words. Given the present results, we conclude that retrieval-induced forgetting of negative words does not require recollection of episodic memories.

Children and adolescents' performance on a medium-length/nonsemantic word-list test.

PubMed

Flores-Lázaro, Julio César; Salgado Soruco, María Alejandra; Stepanov, Igor I

2017-01-01

Word-list learning tasks are among the most important and frequently used tests for declarative memory evaluation. For example, the California Verbal Learning Test-Children's Version (CVLT-C) and Rey Auditory Verbal Learning Test provide important information about different cognitive-neuropsychological processes. However, the impact of test length (i.e., number of words) and semantic organization (i.e., type of words) on children's and adolescents' memory performance remains to be clarified, especially during this developmental stage. To explore whether a medium-length non-semantically organized test can produce the typical curvilinear performance that semantically organized tests produce, reflecting executive control, we studied and compared the cognitive performance of normal children and adolescents by utilizing mathematical modeling. The model is based on the first-order system transfer function and has been successfully applied to learning curves for the CVLT-C (15 words, semantically organized paradigm). Results indicate that learning nine semantically unrelated words produces typical curvilinear (executive function) performance in children and younger adolescents and that performance could be effectively analyzed with the mathematical model. This indicates that the exponential increase (curvilinear performance) of correctly learned words does not solely depend on semantic and/or length features. This type of test controls semantic and length effects and may represent complementary tools for executive function evaluation in clinical populations in which semantic and/or length processing are affected.

Software analysis in the semantic web

NASA Astrophysics Data System (ADS)

Taylor, Joshua; Hall, Robert T.

2013-05-01

Many approaches in software analysis, particularly dynamic malware analyis, benefit greatly from the use of linked data and other Semantic Web technology. In this paper, we describe AIS, Inc.'s Semantic Extractor (SemEx) component from the Malware Analysis and Attribution through Genetic Information (MAAGI) effort, funded under DARPA's Cyber Genome program. The SemEx generates OWL-based semantic models of high and low level behaviors in malware samples from system call traces generated by AIS's introspective hypervisor, IntroVirtTM. Within MAAGI, these semantic models were used by modules that cluster malware samples by functionality, and construct "genealogical" malware lineages. Herein, we describe the design, implementation, and use of the SemEx, as well as the C2DB, an OWL ontology used for representing software behavior and cyber-environments.

Reply to David Kemmerer's "a critique of Mark D. Allen's 'the preservation of verb subcategory knowledge in a spoken language comprehension deficit'".

PubMed

Allen, Mark D; Owens, Tyler E

2008-07-01

Allen [Allen, M. D. (2005). The preservation of verb subcategory knowledge in a spoken language comprehension deficit. Brain and Language, 95, 255-264] presents evidence from a single patient, WBN, to motivate a theory of lexical processing and representation in which syntactic information may be encoded and retrieved independently of semantic information. In his critique, Kemmerer argues that because Allen depended entirely on preposition-based verb subcategory violations to test WBN's knowledge of correct argument structure, his results, at best, address a "strawman" theory. This argument rests on the assumption that preposition subcategory options are superficial syntactic phenomena which are not represented by argument structure proper. We demonstrate that preposition subcategory is in fact treated as semantically determined argument structure in the theories that Allen evaluated, and thus far from irrelevant. In further discussion of grammatically relevant versus irrelevant semantic features, Kemmerer offers a review of his own studies. However, due to an important design shortcoming in these experiments, we remain unconvinced. Reemphasizing the fact the Allen (2005) never claimed to rule out all semantic contributions to syntax, we propose an improvement in Kemmerer's approach that might provide more satisfactory evidence on the distinction between the kinds of relevant versus irrelevant features his studies have addressed.

Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues

NASA Astrophysics Data System (ADS)

Adams, W. H.; Iyengar, Giridharan; Lin, Ching-Yung; Naphade, Milind Ramesh; Neti, Chalapathy; Nock, Harriet J.; Smith, John R.

2003-12-01

We present a learning-based approach to the semantic indexing of multimedia content using cues derived from audio, visual, and text features. We approach the problem by developing a set of statistical models for a predefined lexicon. Novel concepts are then mapped in terms of the concepts in the lexicon. To achieve robust detection of concepts, we exploit features from multiple modalities, namely, audio, video, and text. Concept representations are modeled using Gaussian mixture models (GMM), hidden Markov models (HMM), and support vector machines (SVM). Models such as Bayesian networks and SVMs are used in a late-fusion approach to model concepts that are not explicitly modeled in terms of features. Our experiments indicate promise in the proposed classification and fusion methodologies: our proposed fusion scheme achieves more than 10% relative improvement over the best unimodal concept detector.

Statistics and dynamics of attractor networks with inter-correlated patterns

NASA Astrophysics Data System (ADS)

Kropff, E.

2007-02-01

In an embodied feature representation view, the semantic memory represents concepts in the brain by the associated activation of the features that describe it, each one of them processed in a differentiated region of the cortex. This system has been modeled with a Potts attractor network. Several studies of feature representation show that the correlation between patterns plays a crucial role in semantic memory. The present work focuses on two aspects of the effect of correlations in attractor networks. In first place, it assesses how a Potts network can store a set of patterns with non-trivial correlations between them. This is done through a simple and biologically plausible modification to the classical learning rule. In second place, it studies the complexity of latching transitions between attractor states, and how this complexity can be controlled.

Semantic Metrics for Analysis of Software

NASA Technical Reports Server (NTRS)

Etzkorn, Letha H.; Cox, Glenn W.; Farrington, Phil; Utley, Dawn R.; Ghalston, Sampson; Stein, Cara

2005-01-01

A recently conceived suite of object-oriented software metrics focus is on semantic aspects of software, in contradistinction to traditional software metrics, which focus on syntactic aspects of software. Semantic metrics represent a more human-oriented view of software than do syntactic metrics. The semantic metrics of a given computer program are calculated by use of the output of a knowledge-based analysis of the program, and are substantially more representative of software quality and more readily comprehensible from a human perspective than are the syntactic metrics.

Semantic wireless localization of WiFi terminals in smart buildings

NASA Astrophysics Data System (ADS)

Ahmadi, H.; Polo, A.; Moriyama, T.; Salucci, M.; Viani, F.

2016-06-01

The wireless localization of mobile terminals in indoor scenarios by means of a semantic interpretation of the environment is addressed in this work. A training-less approach based on the real-time calibration of a simple path loss model is proposed which combines (i) the received signal strength information measured by the wireless terminal and (ii) the topological features of the localization domain. A customized evolutionary optimization technique has been designed to estimate the optimal target position that fits the complex wireless indoor propagation and the semantic target-environment relation, as well. The proposed approach is experimentally validated in a real building area where the available WiFi network is opportunistically exploited for data collection. The presented results point out a reduction of the localization error obtained with the introduction of a very simple semantic interpretation of the considered scenario.

RelFinder: Revealing Relationships in RDF Knowledge Bases

NASA Astrophysics Data System (ADS)

Heim, Philipp; Hellmann, Sebastian; Lehmann, Jens; Lohmann, Steffen; Stegemann, Timo

The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

Precision Timing and Measurement for Inference with Laser and Vision

DTIC Science & Technology

2010-01-01

Robotics 24, 8-9 (2007), 699–722. [84] Nüchter, A., Surmann, H., Lingemann, K., and Hertzberg, J. Semantic scene analysis of scanned 3D indoor...Delaunay based triangulation can be ob- tained. Popping the points back to their 3D locations yields a feature sensitive surface mesh . Sequeira et al... texture maps acquired from cameras, the eye can be fooled into believing that the mesh quality is better than it really is. Range images are used to

A computational modeling of semantic knowledge in reading comprehension: Integrating the landscape model with latent semantic analysis.

PubMed

Yeari, Menahem; van den Broek, Paul

2016-09-01

It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena.

High performance semantic factoring of giga-scale semantic graph databases.

DOE Office of Scientific and Technical Information (OSTI.GOV)

al-Saffar, Sinan; Adolf, Bob; Haglin, David

2010-10-01

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to bring high performance computational resources to bear on their analysis, interpretation, and visualization, especially with respect to their innate semantic structure. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multithreaded architecture of the Cray XMT platform, conventional clusters, and large data stores. In this paper we describe that architecture, and present the results of our deployingmore » that for the analysis of the Billion Triple dataset with respect to its semantic factors, including basic properties, connected components, namespace interaction, and typed paths.« less

Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

PubMed Central

Jacobs, Arthur M.

2017-01-01

In this paper I would like to pave the ground for future studies in Computational Stylistics and (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight tentative word features is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the aesthetic potential is proposed. Application of machine learning algorithms fed with this QNA data shows that a classifier of the decision tree family excellently learns to split words into beautiful vs. ugly ones. The results shed light on surface and semantic features theoretically relevant for affective-aesthetic processes in literary reading and generate quantitative predictions for neuroaesthetic studies of verbal materials. PMID:29311877

Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective.

PubMed

Jacobs, Arthur M

2017-01-01

In this paper I would like to pave the ground for future studies in Computational Stylistics and (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight tentative word features is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the aesthetic potential is proposed. Application of machine learning algorithms fed with this QNA data shows that a classifier of the decision tree family excellently learns to split words into beautiful vs. ugly ones. The results shed light on surface and semantic features theoretically relevant for affective-aesthetic processes in literary reading and generate quantitative predictions for neuroaesthetic studies of verbal materials.

An Analysis of the Features of Japanese Technological Terms from the Viewpoint of International Student Education

NASA Astrophysics Data System (ADS)

Matsuda, Makiko; Takahashi, Tomoe; Yukawa, Takashi; Mikami, Yoshiki

In this paper, we analyzed the features of the adoption strategy in creating Japanese technological terms, by comparing with those of other Asian countries. In addition, we investigated how those technological terms are used differently in each engineering field. As a result, in adopting technological terms, phonetic adoption is found more often in Japan (especially in the new research fields like IT) . In contrast, semantic adoption is found more often in China and Vietnam. It is also found that architecture field shares many terms with civil engineering, and electrical field shares many terms with communication. Those findings are quite useful to the engineering education for foreign students.

Verbal fluency in bipolar disorders: A systematic review and meta-analysis.

PubMed

Raucher-Chéné, Delphine; Achim, Amélie M; Kaladjian, Arthur; Besche-Richard, Chrystel

2017-01-01

One of the main features of bipolar disorder (BD), besides mood dysregulation, is an alteration of the structure of language. Bipolar patients present changes in semantic contents, impaired verbal associations, abnormal prosody and abnormal speed of language highlighted with various experimental tasks. Verbal fluency tasks are widely used to assess the abilities of bipolar patients to retrieve and produce verbal material from the lexico-semantic memory. Studies using these tasks have however yielded discrepant results. The aim of this study was thus to determine the extent of the verbal fluency impairment in BD patients and to evaluate if the deficits are affected by the type of task or by mood states. A systematic literature search was conducted in MEDLINE, EBSCOHost and Google Scholar and relevant data were submitted to a meta-analysis. Thirty-nine studies were retained providing data for 52 independent groups of BD patients. The overall meta-analysis revealed a moderate verbal fluency impairment in BD compared to healthy controls (effect size d=0.61). Comparisons between mood states showed significant differences only between euthymic and manic patients and only on category fluency performances. This review is limited by the heterogeneity between studies for the characteristics of BD populations. Also, few of the retained studies examined depressive or mixed episodes. This work confirms that BD patients present with moderate verbal fluency impairments, and underlines the specific effect of mood state on category fluency. This emphasizes the need to distinguish semantic from phonological processes in verbal fluency assessments in BD. Copyright © 2016 Elsevier B.V. All rights reserved.

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates

PubMed Central

Kuhlmann, Michael; Hofmann, Markus J.; Jacobs, Arthur M.

2017-01-01

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks. PMID:28348538

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates.

PubMed

Kuhlmann, Michael; Hofmann, Markus J; Jacobs, Arthur M

2017-01-01

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks.

Get rich quick: the signal to respond procedure reveals the time course of semantic richness effects during visual word recognition.

PubMed

Hargreaves, Ian S; Pexman, Penny M

2014-05-01

According to several current frameworks, semantic processing involves an early influence of language-based information followed by later influences of object-based information (e.g., situated simulations; Santos, Chaigneau, Simmons, & Barsalou, 2011). In the present study we examined whether these predictions extend to the influence of semantic variables in visual word recognition. We investigated the time course of semantic richness effects in visual word recognition using a signal-to-respond (STR) paradigm fitted to a lexical decision (LDT) and a semantic categorization (SCT) task. We used linear mixed effects to examine the relative contributions of language-based (number of senses, ARC) and object-based (imageability, number of features, body-object interaction ratings) descriptions of semantic richness at four STR durations (75, 100, 200, and 400ms). Results showed an early influence of number of senses and ARC in the SCT. In both LDT and SCT, object-based effects were the last to influence participants' decision latencies. We interpret our results within a framework in which semantic processes are available to influence word recognition as a function of their availability over time, and of their relevance to task-specific demands. Copyright © 2014 Elsevier B.V. All rights reserved.

Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Babacan, K.; Chen, L.; Sohn, G.

2017-11-01

As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

Measuring content overlap during handoff communication using distributional semantics: An exploratory study.

PubMed

Abraham, Joanna; Kannampallil, Thomas G; Srinivasan, Vignesh; Galanter, William L; Tagney, Gail; Cohen, Trevor

2017-01-01

We develop and evaluate a methodological approach to measure the degree and nature of overlap in handoff communication content within and across clinical professions. This extensible, exploratory approach relies on combining techniques from conversational analysis and distributional semantics. We audio-recorded handoff communication of residents and nurses on the General Medicine floor of a large academic hospital (n=120 resident and n=120 nurse handoffs). We measured semantic similarity, a proxy for content overlap, between resident-resident and nurse-nurse communication using multiple steps: a qualitative conversational content analysis; an automated semantic similarity analysis using Reflective Random Indexing (RRI); and comparing semantic similarity generated by RRI analysis with human ratings of semantic similarity. There was significant association between the semantic similarity as computed by the RRI method and human rating (ρ=0.88). Based on the semantic similarity scores, content overlap was relatively higher for content related to patient active problems, assessment of active problems, patient-identifying information, past medical history, and medications/treatments. In contrast, content overlap was limited on content related to allergies, family-related information, code status, and anticipatory guidance. Our approach using RRI analysis provides new opportunities for characterizing the nature and degree of overlap in handoff communication. Although exploratory, this method provides a basis for identifying content that can be used for determining shared understanding across clinical professions. Additionally, this approach can inform the development of flexibly standardized handoff tools that reflect clinical content that are most appropriate for fostering shared understanding during transitions of care. Copyright © 2016 Elsevier Inc. All rights reserved.

Enabling Energy-Awareness in the Semantic 3d City Model of Vienna

NASA Astrophysics Data System (ADS)

Agugiaro, G.

2016-09-01

This paper presents and discusses the first results regarding selection, analysis, preparation and eventual integration of a number of energy-related datasets, chosen in order to enrich a CityGML-based semantic 3D city model of Vienna. CityGML is an international standard conceived specifically as information and data model for semantic city models at urban and territorial scale. The still-in-development Energy Application Domain Extension (ADE) is a CityGML extension conceived to specifically model, manage and store energy-related features and attributes for buildings. The work presented in this paper is embedded within the European Marie-Curie ITN project "CINERGY, Smart cities with sustainable energy systems", which aims, among the rest, at developing urban decision making and operational optimisation software tools to minimise non-renewable energy use in cities. Given the scope and scale of the project, it is therefore vital to set up a common, unique and spatio-semantically coherent urban data model to be used as information hub for all applications being developed. This paper reports about the experiences done so far, it describes the test area in Vienna, Austria, and the available data sources, it shows and exemplifies the main data integration issues, the strategies developed to solve them in order to obtain the enriched 3D city model. The first results as well as some comments about their quality and limitations are presented, together with the discussion regarding the next steps and some planned improvements.

Semantic and Visual Memory After Alcohol Abuse.

ERIC Educational Resources Information Center

Donat, Dennis C.

1986-01-01

Compared the relative performance of 40 patients with a history of alcohol abuse on tasks of short-term semantic and visual memory. Performance on the visual memory tasks was impaired significantly relative to the semantic memory task in a within-subjects analysis of variance. Semantic memory was unimpaired. (Author/ABB)

High Performance Descriptive Semantic Analysis of Semantic Graph Databases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to understand their inherent semantic structure, whether codified in explicit ontologies or not. Our group is researching novel methods for what we call descriptive semantic analysis of RDF triplestores, to serve purposes of analysis, interpretation, visualization, and optimization. But data size and computational complexity makes it increasingly necessary to bring high performance computational resources to bear on this task. Our research group built a novel high performance hybrid system comprisingmore » computational capability for semantic graph database processing utilizing the large multi-threaded architecture of the Cray XMT platform, conventional servers, and large data stores. In this paper we describe that architecture and our methods, and present the results of our analyses of basic properties, connected components, namespace interaction, and typed paths such for the Billion Triple Challenge 2010 dataset.« less

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation.

PubMed

Bolleman, Jerven T; Mungall, Christopher J; Strozzi, Francesco; Baran, Joachim; Dumontier, Michel; Bonnal, Raoul J P; Buels, Robert; Hoehndorf, Robert; Fujisawa, Takatomo; Katayama, Toshiaki; Cock, Peter J A

2016-06-13

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned "omics" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

DOE PAGES

Bolleman, Jerven T.; Mungall, Christopher J.; Strozzi, Francesco; ...

2016-06-13

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. In this paper, we have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data formatmore » to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Finally, data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.« less

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolleman, Jerven T.; Mungall, Christopher J.; Strozzi, Francesco

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. In this paper, we have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data formatmore » to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Finally, data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.« less

Knowledge-Base Semantic Gap Analysis for the Vulnerability Detection

NASA Astrophysics Data System (ADS)

Wu, Raymond; Seki, Keisuke; Sakamoto, Ryusuke; Hisada, Masayuki

Web security became an alert in internet computing. To cope with ever-rising security complexity, semantic analysis is proposed to fill-in the gap that the current approaches fail to commit. Conventional methods limit their focus to the physical source codes instead of the abstraction of semantics. It bypasses new types of vulnerability and causes tremendous business loss.

Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

ERIC Educational Resources Information Center

Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

2009-01-01

To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

Building a Semantic Framework for eScience

NASA Astrophysics Data System (ADS)

Movva, S.; Ramachandran, R.; Maskey, M.; Li, X.

2009-12-01

The e-Science vision focuses on the use of advanced computing technologies to support scientists. Recent research efforts in this area have focused primarily on “enabling” use of infrastructure resources for both data and computational access especially in Geosciences. One of the existing gaps in the existing e-Science efforts has been the failure to incorporate stable semantic technologies within the design process itself. In this presentation, we describe our effort in designing a framework for e-Science built using Service Oriented Architecture. Our framework provides users capabilities to create science workflows and mine distributed data. Our e-Science framework is being designed around a mass market tool to promote reusability across many projects. Semantics is an integral part of this framework and our design goal is to leverage the latest stable semantic technologies. The use of these stable semantic technologies will provide the users of our framework the useful features such as: allow search engines to find their content with RDFa tags; create RDF triple data store for their content; create RDF end points to share with others; and semantically mash their content with other online content available as RDF end point.

Categorizing words through semantic memory navigation

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, J.; Arenas, A.

2010-03-01

Semantic memory is the cognitive system devoted to storage and retrieval of conceptual knowledge. Empirical data indicate that semantic memory is organized in a network structure. Everyday experience shows that word search and retrieval processes provide fluent and coherent speech, i.e. are efficient. This implies either that semantic memory encodes, besides thousands of words, different kind of links for different relationships (introducing greater complexity and storage costs), or that the structure evolves facilitating the differentiation between long-lasting semantic relations from incidental, phenomenological ones. Assuming the latter possibility, we explore a mechanism to disentangle the underlying semantic backbone which comprises conceptual structure (extraction of categorical relations between pairs of words), from the rest of information present in the structure. To this end, we first present and characterize an empirical data set modeled as a network, then we simulate a stochastic cognitive navigation on this topology. We schematize this latter process as uncorrelated random walks from node to node, which converge to a feature vectors network. By doing so we both introduce a novel mechanism for information retrieval, and point at the problem of category formation in close connection to linguistic and non-linguistic experience.

Lexical-semantic body knowledge in 5- to 11-year-old children: How spatial body representation influences body semantics.

PubMed

Auclair, Laurent; Jambaqué, Isabelle

2015-01-01

This study addresses the relation between lexico-semantic body knowledge (i.e., body semantics) and spatial body representation (i.e., structural body representation) by analyzing naming performances as a function of body structural topography. One hundred and forty-one children ranging from 5 years 2 months to 10 years 5 months old were asked to provide a lexical label for isolated body part pictures. We compared the children's naming performances according to the location of the body parts (body parts vs. head features and also upper vs. lower limbs) or to their involvement in motor skills (distal segments, joints, and broader body parts). The results showed that the children's naming performance was better for facial body parts than for other body parts. Furthermore, it was found that the naming of body parts was better for body parts related to action. These findings suggest that the development of a spatial body representation shapes the elaboration of semantic body representation processing. Moreover, this influence was not limited to younger children. In our discussion of these results, we focus on the important role of action in the development of body representations and semantic organization.

Integrating Conceptual Knowledge Within and Across Representational Modalities

PubMed Central

McNorgan, Chris; Reid, Jackie; McRae, Ken

2011-01-01

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants’ knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual feature verification task. The pattern of decision latencies across Experiments 1 to 4 is consistent with a deep integration hierarchy. PMID:21093853

The Role of Surface, Semantic and Grammatical Features on Simplification of Spanish Medical Texts: A User Study

PubMed Central

Mukherjee, Partha; Leroy, Gondy; Kauchak, David; Navarrete, Brianda Armenta; Diaz, Damian Y.; Colina, Sonia

2017-01-01

Simplifying medical texts facilitates readability and comprehension. While most simplification work focuses on English, we investigate whether features important for simplifying English text are similarly helpful for simplifying Spanish text. We conducted a user study on 15 Spanish medical texts using Amazon Mechanical Turk and measured perceived and actual difficulty. Using the median of the difficulty scores, we split the texts into easy and difficult groups and extracted 10 surface, 2 semantic and 4 grammatical features. Using t-tests, we identified those features that significantly distinguish easy text from difficult text in Spanish and compare with prior work in English. We found that easy Spanish texts use more repeated words and adverbs, less negations and more familiar words, similar to English. Also like English, difficult Spanish texts use more nouns and adjectives. However in contrast to English, easier Spanish texts contained longer sentences and used grammatical structures that were more varied. PMID:29854201

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W

2016-08-01

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

Feature hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

A Semantic Prosody Analysis of Three Adjective Synonymous Pairs in COCA

ERIC Educational Resources Information Center

Hu, H. C. Marcella

2015-01-01

Over the past two decades the concept of semantic prosody has attracted considerable research interest since Sinclair (1991) observed that "many uses of words and phrases show a tendency to occur in a certain semantic environment" (p. 112). Sinclair (2003) also noted that semantic prosody conveys its pragmatic meaning and attitudinal…

Extending Primitive Spatial Data Models to Include Semantics

NASA Astrophysics Data System (ADS)

Reitsma, F.; Batcheller, J.

2009-04-01

Our traditional geospatial data model involves associating some measurable quality, such as temperature, or observable feature, such as a tree, with a point or region in space and time. When capturing data we implicitly subscribe to some kind of conceptualisation. If we can make this explicit in an ontology and associate it with the captured data, we can leverage formal semantics to reason with the concepts represented in our spatial data sets. To do so, we extend our fundamental representation of geospatial data in a data model by including a URI in our basic data model that links it to our ontology defining our conceptualisation, We thus extend Goodchild et al's geo-atom [1] with the addition of a URI: (x, Z, z(x), URI) . This provides us with pixel or feature level knowledge and the ability to create layers of data from a set of pixels or features that might be drawn from a database based on their semantics. Using open source tools, we present a prototype that involves simple reasoning as a proof of concept. References [1] M.F. Goodchild, M. Yuan, and T.J. Cova. Towards a general theory of geographic representation in gis. International Journal of Geographical Information Science, 21(3):239-260, 2007.

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

PubMed

Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J; Schabath, Matthew B

2017-11-10

The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.

Causal Evidence for a Mechanism of Semantic Integration in the Angular Gyrus as Revealed by High-Definition Transcranial Direct Current Stimulation

PubMed Central

Peelle, Jonathan E.; Bonner, Michael F.; Grossman, Murray

2016-01-01

A defining aspect of human cognition is the ability to integrate conceptual information into complex semantic combinations. For example, we can comprehend “plaid” and “jacket” as individual concepts, but we can also effortlessly combine these concepts to form the semantic representation of “plaid jacket.” Many neuroanatomic models of semantic memory propose that heteromodal cortical hubs integrate distributed semantic features into coherent representations. However, little work has specifically examined these proposed integrative mechanisms and the causal role of these regions in semantic integration. Here, we test the hypothesis that the angular gyrus (AG) is critical for integrating semantic information by applying high-definition transcranial direct current stimulation (tDCS) to an fMRI-guided region-of-interest in the left AG. We found that anodal stimulation to the left AG modulated semantic integration but had no effect on a letter-string control task. Specifically, anodal stimulation to the left AG resulted in faster comprehension of semantically meaningful combinations like “tiny radish” relative to non-meaningful combinations, such as “fast blueberry,” when compared to the effects observed during sham stimulation and stimulation to a right-hemisphere control brain region. Moreover, the size of the effect from brain stimulation correlated with the degree of semantic coherence between the word pairs. These findings demonstrate that the left AG plays a causal role in the integration of lexical-semantic information, and that high-definition tDCS to an associative cortical hub can selectively modulate integrative processes in semantic memory. SIGNIFICANCE STATEMENT A major goal of neuroscience is to understand the neural basis of behaviors that are fundamental to human intelligence. One essential behavior is the ability to integrate conceptual knowledge from semantic memory, allowing us to construct an almost unlimited number of complex concepts from a limited set of basic constituents (e.g., “leaf” and “wet” can be combined into the more complex representation “wet leaf”). Here, we present a novel approach to studying integrative processes in semantic memory by applying focal brain stimulation to a heteromodal cortical hub implicated in semantic processing. Our findings demonstrate a causal role of the left angular gyrus in lexical-semantic integration and provide motivation for novel therapeutic applications in patients with lexical-semantic deficits. PMID:27030767

Causal Evidence for a Mechanism of Semantic Integration in the Angular Gyrus as Revealed by High-Definition Transcranial Direct Current Stimulation.

PubMed

Price, Amy Rose; Peelle, Jonathan E; Bonner, Michael F; Grossman, Murray; Hamilton, Roy H

2016-03-30

A defining aspect of human cognition is the ability to integrate conceptual information into complex semantic combinations. For example, we can comprehend "plaid" and "jacket" as individual concepts, but we can also effortlessly combine these concepts to form the semantic representation of "plaid jacket." Many neuroanatomic models of semantic memory propose that heteromodal cortical hubs integrate distributed semantic features into coherent representations. However, little work has specifically examined these proposed integrative mechanisms and the causal role of these regions in semantic integration. Here, we test the hypothesis that the angular gyrus (AG) is critical for integrating semantic information by applying high-definition transcranial direct current stimulation (tDCS) to an fMRI-guided region-of-interest in the left AG. We found that anodal stimulation to the left AG modulated semantic integration but had no effect on a letter-string control task. Specifically, anodal stimulation to the left AG resulted in faster comprehension of semantically meaningful combinations like "tiny radish" relative to non-meaningful combinations, such as "fast blueberry," when compared to the effects observed during sham stimulation and stimulation to a right-hemisphere control brain region. Moreover, the size of the effect from brain stimulation correlated with the degree of semantic coherence between the word pairs. These findings demonstrate that the left AG plays a causal role in the integration of lexical-semantic information, and that high-definition tDCS to an associative cortical hub can selectively modulate integrative processes in semantic memory. A major goal of neuroscience is to understand the neural basis of behaviors that are fundamental to human intelligence. One essential behavior is the ability to integrate conceptual knowledge from semantic memory, allowing us to construct an almost unlimited number of complex concepts from a limited set of basic constituents (e.g., "leaf" and "wet" can be combined into the more complex representation "wet leaf"). Here, we present a novel approach to studying integrative processes in semantic memory by applying focal brain stimulation to a heteromodal cortical hub implicated in semantic processing. Our findings demonstrate a causal role of the left angular gyrus in lexical-semantic integration and provide motivation for novel therapeutic applications in patients with lexical-semantic deficits. Copyright © 2016 the authors 0270-6474/16/363829-10$15.00/0.

Procedurally Mediated Social Inferences: The Case of Category Accessibility Effects.

DTIC Science & Technology

1984-12-01

New York: Academic. Craik , F. I. M., & Lockhart , R. S. (1972). Levels of processing : A framework for memory research. Journal of Verbal Learning...more "deeply" encoded semantic features (cf. Craik 8 Lockhart , 1972). (A few theorists assume that visual images may also be used as an alternative...semantically rather than phonemically or graphemically ( Craik & Lockhart , 1972). It is this familiar type of declarative memory of which we are usually

Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares

PubMed Central

Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Mur, Marieke

2016-01-01

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (>100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as “human”, “mammal”, and “animal”). The feature-based model includes both object parts (such as “eye”, “tail”, and “handle”) and other descriptive features (such as “circular”, “green”, and “stubbly”). We used non-negative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation. Similarity judgments contain additional categorical variance that is not explained by visual features, reflecting a higher-level more purely semantic representation. PMID:26493748

Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares.

PubMed

Jozwik, Kamila M; Kriegeskorte, Nikolaus; Mur, Marieke

2016-03-01

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (>100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as "human", "mammal", and "animal"). The feature-based model includes both object parts (such as "eye", "tail", and "handle") and other descriptive features (such as "circular", "green", and "stubbly"). We used non-negative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation. Similarity judgments contain additional categorical variance that is not explained by visual features, reflecting a higher-level more purely semantic representation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Semantic Pattern Analysis for Verbal Fluency Based Assessment of Neurological Disorders

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Ainsworth, Keela C; Brown, Tyler C

In this paper, we present preliminary results of semantic pattern analysis of verbal fluency tests used for assessing cognitive psychological and neuropsychological disorders. We posit that recent advances in semantic reasoning and artificial intelligence can be combined to create a standardized computer-aided diagnosis tool to automatically evaluate and interpret verbal fluency tests. Towards that goal, we derive novel semantic similarity (phonetic, phonemic and conceptual) metrics and present the predictive capability of these metrics on a de-identified dataset of participants with and without neurological disorders.

Revealing the Dynamic Modulations That Underpin a Resilient Neural Network for Semantic Cognition: An fMRI Investigation in Patients With Anterior Temporal Lobe Resection.

PubMed

Rice, Grace E; Caswell, Helen; Moore, Perry; Lambon Ralph, Matthew A; Hoffman, Paul

2018-06-06

One critical feature of any well-engineered system is its resilience to perturbation and minor damage. The purpose of the current study was to investigate how resilience is achieved in higher cognitive systems, which we explored through the domain of semantic cognition. Convergent evidence implicates the bilateral anterior temporal lobes (ATLs) as a conceptual knowledge hub. While bilateral damage to this region produces profound semantic impairment, unilateral atrophy/resection or transient perturbation has a limited effect. Two neural mechanisms might underpin this resilience to unilateral ATL damage: 1) the undamaged ATL upregulates its activation in order to compensate; and/or 2) prefrontal regions involved in control of semantic retrieval upregulate to compensate for the impoverished semantic representations that follow from ATL damage. To test these possibilities, 34 postsurgical temporal lobe epilepsy patients and 20 age-matched controls were scanned whilst completing semantic tasks. Pictorial tasks, which produced bilateral frontal and temporal activation, showed few activation differences between patients and control participants. Written word tasks, however, produced a left-lateralized activation pattern and greater differences between the groups. Patients with right ATL resection increased activation in left inferior frontal gyrus (IFG). Patients with left ATL resection upregulated both the right ATL and right IFG. Consistent with recent computational models, these results indicate that 1) written word semantic processing in patients with ATL resection is supported by upregulation of semantic knowledge and control regions, principally in the undamaged hemisphere, and 2) pictorial semantic processing is less affected, presumably because it draws on a more bilateral network.

Frontal lobe damage impairs process and content in semantic memory: evidence from category-specific effects in progressive non-fluent aphasia.

PubMed

Reilly, Jamie; Rodriguez, Amy D; Peelle, Jonathan E; Grossman, Murray

2011-06-01

Portions of left inferior frontal cortex have been linked to semantic memory both in terms of the content of conceptual representation (e.g., motor aspects in an embodied semantics framework) and the cognitive processes used to access these representations (e.g., response selection). Progressive non-fluent aphasia (PNFA) is a neurodegenerative condition characterized by progressive atrophy of left inferior frontal cortex. PNFA can, therefore, provide a lesion model for examining the impact of frontal lobe damage on semantic processing and content. In the current study we examined picture naming in a cohort of PNFA patients across a variety of semantic categories. An embodied approach to semantic memory holds that sensorimotor features such as self-initiated action may assume differential importance for the representation of manufactured artifacts (e.g., naming hand tools). Embodiment theories might therefore predict that patients with frontal damage would be differentially impaired on manufactured artifacts relative to natural kinds, and this prediction was borne out. We also examined patterns of naming errors across a wide range of semantic categories and found that naming error distributions were heterogeneous. Although PNFA patients performed worse overall on naming manufactured artifacts, there was no reliable relationship between anomia and manipulability across semantic categories. These results add to a growing body of research arguing against a purely sensorimotor account of semantic memory, suggesting instead a more nuanced balance of process and content in how the brain represents conceptual knowledge. Copyright © 2010 Elsevier Srl. All rights reserved.

Developmental changes in the neural influence of sublexical information on semantic processing.

PubMed

Lee, Shu-Hui; Booth, James R; Chou, Tai-Li

2015-07-01

Functional magnetic resonance imaging (fMRI) was used to examine the developmental changes in a group of normally developing children (aged 8-12) and adolescents (aged 13-16) during semantic processing. We manipulated association strength (i.e. a global reading unit) and semantic radical (i.e. a local reading unit) to explore the interaction of lexical and sublexical semantic information in making semantic judgments. In the semantic judgment task, two types of stimuli were used: visually-similar (i.e. shared a semantic radical) versus visually-dissimilar (i.e. did not share a semantic radical) character pairs. Participants were asked to indicate if two Chinese characters, arranged according to association strength, were related in meaning. The results showed greater developmental increases in activation in left angular gyrus (BA 39) in the visually-similar compared to the visually-dissimilar pairs for the strong association. There were also greater age-related increases in angular gyrus for the strong compared to weak association in the visually-similar pairs. Both of these results suggest that shared semantics at the sublexical level facilitates the integration of overlapping features at the lexical level in older children. In addition, there was a larger developmental increase in left posterior middle temporal gyrus (BA 21) for the weak compared to strong association in the visually-dissimilar pairs, suggesting conflicting sublexical information placed greater demands on access to lexical representations in the older children. All together, these results suggest that older children are more sensitive to sublexical information when processing lexical representations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Temporal Representation in Semantic Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levandoski, J J; Abdulla, G M

2007-08-07

A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.

Getting connected: Both associative and semantic links structure semantic memory for newly learned persons.

PubMed

Wiese, Holger; Schweinberger, Stefan R

2015-01-01

The present study examined whether semantic memory for newly learned people is structured by visual co-occurrence, shared semantics, or both. Participants were trained with pairs of simultaneously presented (i.e., co-occurring) preexperimentally unfamiliar faces, which either did or did not share additionally provided semantic information (occupation, place of living, etc.). Semantic information could also be shared between faces that did not co-occur. A subsequent priming experiment revealed faster responses for both co-occurrence/no shared semantics and no co-occurrence/shared semantics conditions, than for an unrelated condition. Strikingly, priming was strongest in the co-occurrence/shared semantics condition, suggesting additive effects of these factors. Additional analysis of event-related brain potentials yielded priming in the N400 component only for combined effects of visual co-occurrence and shared semantics, with more positive amplitudes in this than in the unrelated condition. Overall, these findings suggest that both semantic relatedness and visual co-occurrence are important when novel information is integrated into person-related semantic memory.

Semantic Mediation via Access Broker: the OWS-9 experiment

NASA Astrophysics Data System (ADS)

Santoro, Mattia; Papeschi, Fabrizio; Craglia, Massimo; Nativi, Stefano

2013-04-01

Even with the use of common data models standards to publish and share geospatial data, users may still face semantic inconsistencies when they use Spatial Data Infrastructures - especially in multidisciplinary contexts. Several semantic mediation solutions exist to address this issue; they span from simple XSLT documents to transform from one data model schema to another, to more complex services based on the use of ontologies. This work presents the activity done in the context of the OGC Web Services Phase 9 (OWS-9) Cross Community Interoperability to develop a semantic mediation solution by enhancing the GEOSS Discovery and Access Broker (DAB). This is a middleware component that provides harmonized access to geospatial datasets according to client applications preferred service interface (Nativi et al. 2012, Vaccari et al. 2012). Given a set of remote feature data encoded in different feature schemas, the objective of the activity was to use the DAB to enable client applications to transparently access the feature data according to one single schema. Due to the flexible architecture of the Access Broker, it was possible to introduce a new transformation type in the configured chain of transformations. In fact, the Access Broker already provided the following transformations: Coordinate Reference System (CRS), spatial resolution, spatial extent (e.g., a subset of a data set), and data encoding format. A new software module was developed to invoke the needed external semantic mediation service and harmonize the accessed features. In OWS-9 the Access Broker invokes a SPARQL WPS to retrieve mapping rules for the OWS-9 schemas: USGS, and NGA schema. The solution implemented to address this problem shows the flexibility and extensibility of the brokering framework underpinning the GEO DAB: new services can be added to augment the number of supported schemas without the need to modify other components and/or software modules. Moreover, all other transformations (CRS, format, etc.) are available for client applications in a transparent way. Notwithstanding the encouraging results of this experiment, some issues (e.g. the automatic discovery of semantic mediation services to be invoked) still need to be solved. Future work will consider new semantic mediation services to broker, and compliance tests with the INSPIRE transformation service. References: Nativi S., Craglia M. and Pearlman J. 2012. The Brokering Approach for Multidisciplinary Interoperability: A Position Paper. International Journal of Spatial Data Infrastructures Research, Vol. 7, 1-15. http://ijsdir.jrc.ec.europa.eu/index.php/ijsdir/article/view/281/319 Vaccari L., Craglia M., Fugazza C. Nativi S. and Santoro M. 2012. Integrative Research: The EuroGEOSS Experience. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5 (6) 1603-1611. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6187671&contentType=Journals+%26+Magazines&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6383184%29

Supervised Semantic Classification for Nuclear Proliferation Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

Query Auto-Completion Based on Word2vec Semantic Similarity

NASA Astrophysics Data System (ADS)

Shao, Taihua; Chen, Honghui; Chen, Wanyu

2018-04-01

Query auto-completion (QAC) is the first step of information retrieval, which helps users formulate the entire query after inputting only a few prefixes. Regarding the models of QAC, the traditional method ignores the contribution from the semantic relevance between queries. However, similar queries always express extremely similar search intention. In this paper, we propose a hybrid model FS-QAC based on query semantic similarity as well as the query frequency. We choose word2vec method to measure the semantic similarity between intended queries and pre-submitted queries. By combining both features, our experiments show that FS-QAC model improves the performance when predicting the user’s query intention and helping formulate the right query. Our experimental results show that the optimal hybrid model contributes to a 7.54% improvement in terms of MRR against a state-of-the-art baseline using the public AOL query logs.

When semantics aids phonology: A processing advantage for iconic word forms in aphasia.

PubMed

Meteyard, Lotte; Stoppard, Emily; Snudden, Dee; Cappa, Stefano F; Vigliocco, Gabriella

2015-09-01

Iconicity is the non-arbitrary relation between properties of a phonological form and semantic content (e.g. "moo", "splash"). It is a common feature of both spoken and signed languages, and recent evidence shows that iconic forms confer an advantage during word learning. We explored whether iconic forms conferred a processing advantage for 13 individuals with aphasia following left-hemisphere stroke. Iconic and control words were compared in four different tasks: repetition, reading aloud, auditory lexical decision and visual lexical decision. An advantage for iconic words was seen for some individuals in all tasks, with consistent group effects emerging in reading aloud and auditory lexical decision. Both these tasks rely on mapping between semantics and phonology. We conclude that iconicity aids spoken word processing for individuals with aphasia. This advantage is due to a stronger connection between semantic information and phonological forms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reliable Adaptive Video Streaming Driven by Perceptual Semantics for Situational Awareness

PubMed Central

Pimentel-Niño, M. A.; Saxena, Paresh; Vazquez-Castro, M. A.

2015-01-01

A novel cross-layer optimized video adaptation driven by perceptual semantics is presented. The design target is streamed live video to enhance situational awareness in challenging communications conditions. Conventional solutions for recreational applications are inadequate and novel quality of experience (QoE) framework is proposed which allows fully controlled adaptation and enables perceptual semantic feedback. The framework relies on temporal/spatial abstraction for video applications serving beyond recreational purposes. An underlying cross-layer optimization technique takes into account feedback on network congestion (time) and erasures (space) to best distribute available (scarce) bandwidth. Systematic random linear network coding (SRNC) adds reliability while preserving perceptual semantics. Objective metrics of the perceptual features in QoE show homogeneous high performance when using the proposed scheme. Finally, the proposed scheme is in line with content-aware trends, by complying with information-centric-networking philosophy and architecture. PMID:26247057

Gazetteer Brokering through Semantic Mediation

NASA Astrophysics Data System (ADS)

Hobona, G.; Bermudez, L. E.; Brackin, R.

2013-12-01

A gazetteer is a geographical directory containing some information regarding places. It provides names, location and other attributes for places which may include points of interest (e.g. buildings, oilfields and boreholes), and other features. These features can be published via web services conforming to the Gazetteer Application Profile of the Web Feature Service (WFS) standard of the Open Geospatial Consortium (OGC). Against the backdrop of advances in geophysical surveys, there has been a significant increase in the amount of data referenced to locations. Gazetteers services have played a significant role in facilitating access to such data, including through provision of specialized queries such as text, spatial and fuzzy search. Recent developments in the OGC have led to advances in gazetteers such as support for multilingualism, diacritics, and querying via advanced spatial constraints (e.g. search by radial search and nearest neighbor). A challenge remaining however, is that gazetteers produced by different organizations have typically been modeled differently. Inconsistencies from gazetteers produced by different organizations may include naming the same feature in a different way, naming the attributes differently, locating the feature in a different location, and providing fewer or more attributes than the other services. The Gazetteer application profile of the WFS is a starting point to address such inconsistencies by providing a standardized interface based on rules specified in ISO 19112, the international standard for spatial referencing by geographic identifiers. The profile, however, does not provide rules to deal with semantic inconsistencies. The USGS and NGA commissioned research into the potential for a Single Point of Entry Global Gazetteer (SPEGG). The research was conducted by the Cross Community Interoperability thread of the OGC testbed, referenced OWS-9. The testbed prototyped approaches for brokering gazetteers through use of semantic web technologies, including ontologies and a semantic mediator. The semantically-enhanced SPEGG allowed a client to submit a single query (e.g. ';hills') and to retrieve data from two separate gazetteers with different vocabularies (e.g. where one refers to ';summits' another refers to ';hills'). Supporting the SPEGG was a SPARQL server that held the ontologies and processed queries on them. Earth Science surveys and forecast always have a place on Earth. Being able to share the information about a place and solve inconsistencies about that place from different sources will enable geoscientists to better do their research. In the advent of mobile geo computing and location based services (LBS), brokering gazetteers will provide geoscientists with access to gazetteer services rich with information and functionality beyond that offered by current generic gazetteers.

Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

PubMed

Zhang, Shu-Bo; Tang, Qiang-Rong

2016-07-21

Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

How to Constrain and Maintain a Lexicon for the Treatment of Progressive Semantic Naming Deficits: Principles of Item Selection for Formal Semantic Therapy

PubMed Central

Reilly, Jamie

2015-01-01

The progressive degradation of semantic memory is a common feature of many forms of dementia, including Alzheimer’s Disease and the semantic variant of Primary Progressive Aphasia (svPPA). One of the most functionally debilitating effects of this semantic impairment is the inability to name common people and objects (i.e., anomia). Clinical management of a progressive, semantically-based anomia presents extraordinary challenge for neurorehabilitation. Techniques such as errorless learning and spaced-retrieval training show promise for retraining forgotten words. However, we lack complementary detail about what to train (i.e., item selection) and how to flexibly adapt the training to a declining cognitive system. In this position paper, I weigh the relative merits of several treatment rationales (e.g., restore vs. compensate) and advocate for maintenance of known words over reacquisition of forgotten knowledge in the context of semantic treatment paradigms. I propose a system for generating an item pool and outline a set of core principles for training and sustaining a micro-lexicon consisting of approximately 100 words. These principles are informed by lessons learned over the course of a Phase I treatment study targeting language maintenance over a 5-year span in Alzheimer’s Disease and Frontotemporal Degeneration. Finally, I propose a semantic training approach that capitalizes on lexical frequency and repeated training on conceptual structure to offset the loss of key vocabulary as disease severity worsens. PMID:25609229

The perirhinal cortex and conceptual processing: Effects of feature-based statistics following damage to the anterior temporal lobes.

PubMed

Wright, Paul; Randall, Billi; Clarke, Alex; Tyler, Lorraine K

2015-09-01

The anterior temporal lobe (ATL) plays a prominent role in models of semantic knowledge, although it remains unclear how the specific subregions within the ATL contribute to semantic memory. Patients with neurodegenerative diseases, like semantic dementia, have widespread damage to the ATL thus making inferences about the relationship between anatomy and cognition problematic. Here we take a detailed anatomical approach to ask which substructures within the ATL contribute to conceptual processing, with the prediction that the perirhinal cortex (PRc) will play a critical role for concepts that are more semantically confusable. We tested two patient groups, those with and without damage to the PRc, across two behavioural experiments - picture naming and word-picture matching. For both tasks, we manipulated the degree of semantic confusability of the concepts. By contrasting the performance of the two groups, along with healthy controls, we show that damage to the PRc results in worse performance in processing concepts with higher semantic confusability across both experiments. Further by correlating the degree of damage across anatomically defined regions of interest with performance, we find that PRc damage is related to performance for concepts with increased semantic confusability. Our results show that the PRc supports a necessary and crucial neurocognitve function that enables fine-grained conceptual processes to take place through the resolution of semantic confusability. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Electrocortical N400 Effects of Semantic Satiation

PubMed Central

Ströberg, Kim; Andersen, Lau M.; Wiens, Stefan

2017-01-01

Semantic satiation is characterised by the subjective and temporary loss of meaning after high repetition of a prime word. To study the nature of this effect, previous electroencephalography (EEG) research recorded the N400, an ERP component that is sensitive to violations of semantic context. The N400 is characterised by a relative negativity to words that are unrelated vs. related to the semantic context. The semantic satiation hypothesis predicts that the N400 should decrease with high repetition. However, previous findings have been inconsistent. Because of these inconsistent findings and the shortcomings of previous research, we used a modified design that minimises confounding effects from non-semantic processes. We recorded 64-channel EEG and analysed the N400 in a semantic priming task in which the primes were repeated 3 or 30 times. Critically, we separated low and high repetition trials and excluded response trials. Further, we varied the physical features (letter case and format) of consecutive primes to minimise confounding effects from perceptual habituation. For centrofrontal electrodes, the N400 was reduced after 30 repetitions (vs. 3 repetitions). Explorative source reconstructions suggested that activity decreased after 30 repetitions in bilateral inferior temporal gyrus, the right posterior section of the superior and middle temporal gyrus, right supramarginal gyrus, bilateral lateral occipital cortex, and bilateral lateral orbitofrontal cortex. These areas overlap broadly with those typically involved in the N400, namely middle temporal gyrus and inferior frontal gyrus. The results support the semantic rather than the perceptual nature of the satiation effect. PMID:29375411

Neurolinguistic approach to natural language processing with applications to medical text analysis.

PubMed

Duch, Włodzisław; Matykiewicz, Paweł; Pestian, John

2008-12-01

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. The approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications.

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.

PubMed

Velupillai, S; Mowery, D; South, B R; Kvist, M; Dalianis, H

2015-08-13

We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

PubMed Central

Mowery, D.; South, B. R.; Kvist, M.; Dalianis, H.

2015-01-01

Summary Objectives We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. Methods We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Results Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. Conclusions There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices. PMID:26293867

Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study

ERIC Educational Resources Information Center

Jorge-Botana, Guillermo; Olmos, Ricardo; Sanjosé, Vicente

2017-01-01

Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of…

An Experiment in Scientific Code Semantic Analysis

NASA Technical Reports Server (NTRS)

Stewart, Mark E. M.

1998-01-01

This paper concerns a procedure that analyzes aspects of the meaning or semantics of scientific and engineering code. This procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and parsing this annotated code using multiple, distributed expert parsers. These semantic parser are designed to recognize formulae in different disciplines including physical and mathematical formulae and geometrical position in a numerical scheme. The parsers will automatically recognize and document some static, semantic concepts and locate some program semantic errors. Results are shown for a subroutine test case and a collection of combustion code routines. This ability to locate some semantic errors and document semantic concepts in scientific and engineering code should reduce the time, risk, and effort of developing and using these codes.

Richer concepts are better remembered: number of features effects in free recall

PubMed Central

Hargreaves, Ian S.; Pexman, Penny M.; Johnson, Jeremy C.; Zdrazilova, Lenka

2012-01-01

Many models of memory build in a term for encoding variability, the observation that there can be variability in the richness or extensiveness of processing at encoding, and that this variability has consequences for retrieval. In four experiments, we tested the expectation that encoding variability could be driven by the properties of the to-be-remembered item. Specifically, that concepts associated with more semantic features would be better remembered than concepts associated with fewer semantic features. Using feature listing norms we selected sets of items for which people tend to list higher numbers of features (high NoF) and items for which people tend to list lower numbers of features (low NoF). Results showed more accurate free recall for high NoF concepts than for low NoF concepts in expected memory tasks (Experiments 1–3) and also in an unexpected memory task (Experiment 4). This effect was not the result of associative chaining between study items (Experiment 3), and can be attributed to the amount of item-specific processing that occurs at study (Experiment 4). These results provide evidence that stimulus-specific differences in processing at encoding have consequences for explicit memory retrieval. PMID:22514526

Semantic Memory in the Clinical Progression of Alzheimer Disease.

PubMed

Tchakoute, Christophe T; Sainani, Kristin L; Henderson, Victor W

2017-09-01

Semantic memory measures may be useful in tracking and predicting progression of Alzheimer disease. We investigated relationships among semantic memory tasks and their 1-year predictive value in women with Alzheimer disease. We conducted secondary analyses of a randomized clinical trial of raloxifene in 42 women with late-onset mild-to-moderate Alzheimer disease. We assessed semantic memory with tests of oral confrontation naming, category fluency, semantic recognition and semantic naming, and semantic density in written narrative discourse. We measured global cognition (Alzheimer Disease Assessment Scale, cognitive subscale), dementia severity (Clinical Dementia Rating sum of boxes), and daily function (Activities of Daily Living Inventory) at baseline and 1 year. At baseline and 1 year, most semantic memory scores correlated highly or moderately with each other and with global cognition, dementia severity, and daily function. Semantic memory task performance at 1 year had worsened one-third to one-half standard deviation. Factor analysis of baseline test scores distinguished processes in semantic and lexical retrieval (semantic recognition, semantic naming, confrontation naming) from processes in lexical search (semantic density, category fluency). The semantic-lexical retrieval factor predicted global cognition at 1 year. Considered separately, baseline confrontation naming and category fluency predicted dementia severity, while semantic recognition and a composite of semantic recognition and semantic naming predicted global cognition. No individual semantic memory test predicted daily function. Semantic-lexical retrieval and lexical search may represent distinct aspects of semantic memory. Semantic memory processes are sensitive to cognitive decline and dementia severity in Alzheimer disease.

Semantic preview benefit during reading.

PubMed

Hohenstein, Sven; Kliegl, Reinhold

2014-01-01

Word features in parafoveal vision influence eye movements during reading. The question of whether readers extract semantic information from parafoveal words was studied in 3 experiments by using a gaze-contingent display change technique. Subjects read German sentences containing 1 of several preview words that were replaced by a target word during the saccade to the preview (boundary paradigm). In the 1st experiment the preview word was semantically related or unrelated to the target. Fixation durations on the target were shorter for semantically related than unrelated previews, consistent with a semantic preview benefit. In the 2nd experiment, half the sentences were presented following the rules of German spelling (i.e., previews and targets were printed with an initial capital letter), and the other half were presented completely in lowercase. A semantic preview benefit was obtained under both conditions. In the 3rd experiment, we introduced 2 further preview conditions, an identical word and a pronounceable nonword, while also manipulating the text contrast. Whereas the contrast had negligible effects, fixation durations on the target were reliably different for all 4 types of preview. Semantic preview benefits were greater for pretarget fixations closer to the boundary (large preview space) and, although not as consistently, for long pretarget fixation durations (long preview time). The results constrain theoretical proposals about eye movement control in reading. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

COEUS: “semantic web in a box” for biomedical applications

PubMed Central

2012-01-01

Background As the “omics” revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter’s complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. Results COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a “semantic web in a box” approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. Conclusions The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/. PMID:23244467

Ontology Alignment Architecture for Semantic Sensor Web Integration

PubMed Central

Fernandez, Susel; Marsa-Maestre, Ivan; Velasco, Juan R.; Alarcos, Bernardo

2013-01-01

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall. PMID:24051523

Ontology alignment architecture for semantic sensor Web integration.

PubMed

Fernandez, Susel; Marsa-Maestre, Ivan; Velasco, Juan R; Alarcos, Bernardo

2013-09-18

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall.

COEUS: "semantic web in a box" for biomedical applications.

PubMed

Lopes, Pedro; Oliveira, José Luís

2012-12-17

As the "omics" revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter's complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a "semantic web in a box" approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/.

Auto-Generated Semantic Processing Services

NASA Technical Reports Server (NTRS)

Davis, Rodney; Hupf, Greg

2009-01-01

Auto-Generated Semantic Processing (AGSP) Services is a suite of software tools for automated generation of other computer programs, denoted cross-platform semantic adapters, that support interoperability of computer-based communication systems that utilize a variety of both new and legacy communication software running in a variety of operating- system/computer-hardware combinations. AGSP has numerous potential uses in military, space-exploration, and other government applications as well as in commercial telecommunications. The cross-platform semantic adapters take advantage of common features of computer- based communication systems to enforce semantics, messaging protocols, and standards of processing of streams of binary data to ensure integrity of data and consistency of meaning among interoperating systems. The auto-generation aspect of AGSP Services reduces development time and effort by emphasizing specification and minimizing implementation: In effect, the design, building, and debugging of software for effecting conversions among complex communication protocols, custom device mappings, and unique data-manipulation algorithms is replaced with metadata specifications that map to an abstract platform-independent communications model. AGSP Services is modular and has been shown to be easily integrable into new and legacy NASA flight and ground communication systems.

A fusion network for semantic segmentation using RGB-D data

NASA Astrophysics Data System (ADS)

Yuan, Jiahui; Zhang, Kun; Xia, Yifan; Qi, Lin; Dong, Junyu

2018-04-01

Semantic scene parsing is considerable in many intelligent field, including perceptual robotics. For the past few years, pixel-wise prediction tasks like semantic segmentation with RGB images has been extensively studied and has reached very remarkable parsing levels, thanks to convolutional neural networks (CNNs) and large scene datasets. With the development of stereo cameras and RGBD sensors, it is expected that additional depth information will help improving accuracy. In this paper, we propose a semantic segmentation framework incorporating RGB and complementary depth information. Motivated by the success of fully convolutional networks (FCN) in semantic segmentation field, we design a fully convolutional networks consists of two branches which extract features from both RGB and depth data simultaneously and fuse them as the network goes deeper. Instead of aggregating multiple model, our goal is to utilize RGB data and depth data more effectively in a single model. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and achieve competitive results with the state-of-the-art methods.

The neural basis for novel semantic categorization.

PubMed

Koenig, Phyllis; Smith, Edward E; Glosser, Guila; DeVita, Chris; Moore, Peachie; McMillan, Corey; Gee, Jim; Grossman, Murray

2005-01-15

We monitored regional cerebral activity with BOLD fMRI during acquisition of a novel semantic category and subsequent categorization of test stimuli by a rule-based strategy or a similarity-based strategy. We observed different patterns of activation in direct comparisons of rule- and similarity-based categorization. During rule-based category acquisition, subjects recruited anterior cingulate, thalamic, and parietal regions to support selective attention to perceptual features, and left inferior frontal cortex to helps maintain rules in working memory. Subsequent rule-based categorization revealed anterior cingulate and parietal activation while judging stimuli whose conformity with the rules was readily apparent, and left inferior frontal recruitment during judgments of stimuli whose conformity was less apparent. By comparison, similarity-based category acquisition showed recruitment of anterior prefrontal and posterior cingulate regions, presumably to support successful retrieval of previously encountered exemplars from long-term memory, and bilateral temporal-parietal activation for perceptual feature integration. Subsequent similarity-based categorization revealed temporal-parietal, posterior cingulate, and anterior prefrontal activation. These findings suggest that large-scale networks support relatively distinct categorization processes during the acquisition and judgment of semantic category knowledge.

Chaotic Dynamics of Linguistic-Like Processes at the Syntactical and Semantic Levels: in the Pursuit of a Multifractal Attractor

NASA Astrophysics Data System (ADS)

Nicolis, John S.; Katsikas, Anastassis A.

Collective parameters such as the Zipf's law-like statistics, the Transinformation, the Block Entropy and the Markovian character are compared for natural, genetic, musical and artificially generated long texts from generating partitions (alphabets) on homogeneous as well as on multifractal chaotic maps. It appears that minimal requirements for a language at the syntactical level such as memory, selectivity of few keywords and broken symmetry in one dimension (polarity) are more or less met by dynamically iterating simple maps or flows e.g. very simple chaotic hardware. The same selectivity is observed at the semantic level where the aim refers to partitioning a set of enviromental impinging stimuli onto coexisting attractors-categories. Under the regime of pattern recognition and classification, few key features of a pattern or few categories claim the lion's share of the information stored in this pattern and practically, only these key features are persistently scanned by the cognitive processor. A multifractal attractor model can in principle explain this high selectivity, both at the syntactical and the semantic levels.

Point Cloud Classification of Tesserae from Terrestrial Laser Data Combined with Dense Image Matching for Archaeological Information Extraction

NASA Astrophysics Data System (ADS)

Poux, F.; Neuville, R.; Billen, R.

2017-08-01

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Lexical Processing in Toddlers with ASD: Does Weak Central Coherence Play a Role?

PubMed

Ellis Weismer, Susan; Haebig, Eileen; Edwards, Jan; Saffran, Jenny; Venker, Courtney E

2016-12-01

This study investigated whether vocabulary delays in toddlers with autism spectrum disorders (ASD) can be explained by a cognitive style that prioritizes processing of detailed, local features of input over global contextual integration-as claimed by the weak central coherence (WCC) theory. Thirty toddlers with ASD and 30 younger, cognition-matched typical controls participated in a looking-while-listening task that assessed whether perceptual or semantic similarities among named images disrupted word recognition relative to a neutral condition. Overlap of perceptual features invited local processing whereas semantic overlap invited global processing. With the possible exception of a subset of toddlers who had very low vocabulary skills, these results provide no evidence that WCC is characteristic of lexical processing in toddlers with ASD.

Gstruct: a system for extracting schemas from GML documents

NASA Astrophysics Data System (ADS)

Chen, Hui; Zhu, Fubao; Guan, Jihong; Zhou, Shuigeng

2008-10-01

Geography Markup Language (GML) becomes the de facto standard for geographic information representation on the internet. GML schema provides a way to define the structure, content, and semantic of GML documents. It contains useful structural information of GML documents and plays an important role in storing, querying and analyzing GML data. However, GML schema is not mandatory, and it is common that a GML document contains no schema. In this paper, we present Gstruct, a tool for GML schema extraction. Gstruct finds the features in the input GML documents, identifies geometry datatypes as well as simple datatypes, then integrates all these features and eliminates improper components to output the optimal schema. Experiments demonstrate that Gstruct is effective in extracting semantically meaningful schemas from GML documents.

A User-Centric Knowledge Creation Model in a Web of Object-Enabled Internet of Things Environment

PubMed Central

Kibria, Muhammad Golam; Fattah, Sheik Mohammad Mostakim; Jeong, Kwanghyeon; Chong, Ilyoung; Jeong, Youn-Kwae

2015-01-01

User-centric service features in a Web of Object-enabled Internet of Things environment can be provided by using a semantic ontology that classifies and integrates objects on the World Wide Web as well as shares and merges context-aware information and accumulated knowledge. The semantic ontology is applied on a Web of Object platform to virtualize the real world physical devices and information to form virtual objects that represent the features and capabilities of devices in the virtual world. Detailed information and functionalities of multiple virtual objects are combined with service rules to form composite virtual objects that offer context-aware knowledge-based services, where context awareness plays an important role in enabling automatic modification of the system to reconfigure the services based on the context. Converting the raw data into meaningful information and connecting the information to form the knowledge and storing and reusing the objects in the knowledge base can both be expressed by semantic ontology. In this paper, a knowledge creation model that synchronizes a service logistic model and a virtual world knowledge model on a Web of Object platform has been proposed. To realize the context-aware knowledge-based service creation and execution, a conceptual semantic ontology model has been developed and a prototype has been implemented for a use case scenario of emergency service. PMID:26393609

Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features.

PubMed

Zhu, Feida; Yan, Zhicheng; Bu, Jiajun; Yu, Yizhou

2017-05-10

Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks (FCNs) for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels (TSPs) to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of datasets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.

A User-Centric Knowledge Creation Model in a Web of Object-Enabled Internet of Things Environment.

PubMed

Kibria, Muhammad Golam; Fattah, Sheik Mohammad Mostakim; Jeong, Kwanghyeon; Chong, Ilyoung; Jeong, Youn-Kwae

2015-09-18

User-centric service features in a Web of Object-enabled Internet of Things environment can be provided by using a semantic ontology that classifies and integrates objects on the World Wide Web as well as shares and merges context-aware information and accumulated knowledge. The semantic ontology is applied on a Web of Object platform to virtualize the real world physical devices and information to form virtual objects that represent the features and capabilities of devices in the virtual world. Detailed information and functionalities of multiple virtual objects are combined with service rules to form composite virtual objects that offer context-aware knowledge-based services, where context awareness plays an important role in enabling automatic modification of the system to reconfigure the services based on the context. Converting the raw data into meaningful information and connecting the information to form the knowledge and storing and reusing the objects in the knowledge base can both be expressed by semantic ontology. In this paper, a knowledge creation model that synchronizes a service logistic model and a virtual world knowledge model on a Web of Object platform has been proposed. To realize the context-aware knowledge-based service creation and execution, a conceptual semantic ontology model has been developed and a prototype has been implemented for a use case scenario of emergency service.

Metaphor, Metonymy, and Their Interaction in the Production of Semantic Approximations by Monolingual Children: A Corpus Analysis

ERIC Educational Resources Information Center

Pérez-Hernández, Lorena; Duvignau, Karine

2016-01-01

The present study looks into the largely unexplored territory of the cognitive underpinnings of semantic approximations in child language. The analysis of a corpus of 233 semantic approximations produced by 101 monolingual French-speaking children from 1;8 to 4;2 years of age leads to a classification of a significant number of them as instances…

Legacy2Drupal: Conversion of an existing relational oceanographic database to a Drupal 7 CMS

NASA Astrophysics Data System (ADS)

Work, T. T.; Maffei, A. R.; Chandler, C. L.; Groman, R. C.

2011-12-01

Content Management Systems (CMSs) such as Drupal provide powerful features that can be of use to oceanographic (and other geo-science) data managers. However, in many instances, geo-science data management offices have already designed and implemented customized schemas for their metadata. The NSF funded Biological Chemical and Biological Data Management Office (BCO-DMO) has ported an existing relational database containing oceanographic metadata, along with an existing interface coded in Cold Fusion middleware, to a Drupal 7 Content Management System. This is an update on an effort described as a proof-of-concept in poster IN21B-1051, presented at AGU2009. The BCO-DMO project has translated all the existing database tables, input forms, website reports, and other features present in the existing system into Drupal CMS features. The replacement features are made possible by the use of Drupal content types, CCK node-reference fields, a custom theme, and a number of other supporting modules. This presentation describes the process used to migrate content in the original BCO-DMO metadata database to Drupal 7, some problems encountered during migration, and the modules used to migrate the content successfully. Strategic use of Drupal 7 CMS features that enable three separate but complementary interfaces to provide access to oceanographic research metadata will also be covered: 1) a Drupal 7-powered user front-end; 2) REST-ful JSON web services (providing a Mapserver interface to the metadata and data; and 3) a SPARQL interface to a semantic representation of the repository metadata (this feeding a new faceted search capability currently under development). The existing BCO-DMO ontology, developed in collaboration with Rensselaer Polytechnic Institute's Tetherless World Constellation, makes strategic use of pre-existing ontologies and will be used to drive semantically-enabled faceted search capabilities planned for the site. At this point, the use of semantic technologies included in the Drupal 7 core is anticipated. Using a public domain CMS as opposed to proprietary middleware, and taking advantage of the many features of Drupal 7 that are designed to support semantically-enabled interfaces will help prepare the BCO-DMO and other science data repositories for interoperability between systems that serve ecosystem research data.

Conceptual control across modalities: graded specialisation for pictures and words in inferior frontal and posterior temporal cortex.

PubMed

Krieger-Redwood, Katya; Teige, Catarina; Davey, James; Hymers, Mark; Jefferies, Elizabeth

2015-09-01

Controlled semantic retrieval to words elicits co-activation of inferior frontal (IFG) and left posterior temporal cortex (pMTG), but research has not yet established (i) the distinct contributions of these regions or (ii) whether the same processes are recruited for non-verbal stimuli. Words have relatively flexible meanings - as a consequence, identifying the context that links two specific words is relatively demanding. In contrast, pictures are richer stimuli and their precise meaning is better specified by their visible features - however, not all of these features will be relevant to uncovering a given association, tapping selection/inhibition processes. To explore potential differences across modalities, we took a commonly-used manipulation of controlled retrieval demands, namely the identification of weak vs. strong associations, and compared word and picture versions. There were 4 key findings: (1) Regions of interest (ROIs) in posterior IFG (BA44) showed graded effects of modality (e.g., words>pictures in left BA44; pictures>words in right BA44). (2) An equivalent response was observed in left mid-IFG (BA45) across modalities, consistent with the multimodal semantic control deficits that typically follow LIFG lesions. (3) The anterior IFG (BA47) ROI showed a stronger response to verbal than pictorial associations, potentially reflecting a role for this region in establishing a meaningful context that can be used to direct semantic retrieval. (4) The left pMTG ROI also responded to difficulty across modalities yet showed a stronger response overall to verbal stimuli, helping to reconcile two distinct literatures that have implicated this site in semantic control and lexical-semantic access respectively. We propose that left anterior IFG and pMTG work together to maintain a meaningful context that shapes ongoing semantic processing, and that this process is more strongly taxed by word than picture associations. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Conceptual control across modalities: graded specialisation for pictures and words in inferior frontal and posterior temporal cortex

PubMed Central

Krieger-Redwood, Katya; Teige, Catarina; Davey, James; Hymers, Mark; Jefferies, Elizabeth

2015-01-01

Controlled semantic retrieval to words elicits co-activation of inferior frontal (IFG) and left posterior temporal cortex (pMTG), but research has not yet established (i) the distinct contributions of these regions or (ii) whether the same processes are recruited for non-verbal stimuli. Words have relatively flexible meanings – as a consequence, identifying the context that links two specific words is relatively demanding. In contrast, pictures are richer stimuli and their precise meaning is better specified by their visible features – however, not all of these features will be relevant to uncovering a given association, tapping selection/inhibition processes. To explore potential differences across modalities, we took a commonly-used manipulation of controlled retrieval demands, namely the identification of weak vs. strong associations, and compared word and picture versions. There were 4 key findings: (1) Regions of interest (ROIs) in posterior IFG (BA44) showed graded effects of modality (e.g., words>pictures in left BA44; pictures>words in right BA44). (2) An equivalent response was observed in left mid-IFG (BA45) across modalities, consistent with the multimodal semantic control deficits that typically follow LIFG lesions. (3) The anterior IFG (BA47) ROI showed a stronger response to verbal than pictorial associations, potentially reflecting a role for this region in establishing a meaningful context that can be used to direct semantic retrieval. (4) The left pMTG ROI also responded to difficulty across modalities yet showed a stronger response overall to verbal stimuli, helping to reconcile two distinct literatures that have implicated this site in semantic control and lexical-semantic access respectively. We propose that left anterior IFG and pMTG work together to maintain a meaningful context that shapes ongoing semantic processing, and that this process is more strongly taxed by word than picture associations. PMID:25726898

Mining semantic networks of bioinformatics e-resources from the literature

PubMed Central

2011-01-01

Background There have been a number of recent efforts (e.g. BioCatalogue, BioMoby) to systematically catalogue bioinformatics tools, services and datasets. These efforts rely on manual curation, making it difficult to cope with the huge influx of various electronic resources that have been provided by the bioinformatics community. We present a text mining approach that utilises the literature to automatically extract descriptions and semantically profile bioinformatics resources to make them available for resource discovery and exploration through semantic networks that contain related resources. Results The method identifies the mentions of resources in the literature and assigns a set of co-occurring terminological entities (descriptors) to represent them. We have processed 2,691 full-text bioinformatics articles and extracted profiles of 12,452 resources containing associated descriptors with binary and tf*idf weights. Since such representations are typically sparse (on average 13.77 features per resource), we used lexical kernel metrics to identify semantically related resources via descriptor smoothing. Resources are then clustered or linked into semantic networks, providing the users (bioinformaticians, curators and service/tool crawlers) with a possibility to explore algorithms, tools, services and datasets based on their relatedness. Manual exploration of links between a set of 18 well-known bioinformatics resources suggests that the method was able to identify and group semantically related entities. Conclusions The results have shown that the method can reconstruct interesting functional links between resources (e.g. linking data types and algorithms), in particular when tf*idf-like weights are used for profiling. This demonstrates the potential of combining literature mining and simple lexical kernel methods to model relatedness between resource descriptors in particular when there are few features, thus potentially improving the resource description, discovery and exploration process. The resource profiles are available at http://gnode1.mib.man.ac.uk/bioinf/semnets.html PMID:21388573

New methods for analyzing semantic graph based assessments in science education

NASA Astrophysics Data System (ADS)

Vikaros, Lance Steven

This research investigated how the scoring of semantic graphs (known by many as concept maps) could be improved and automated in order to address issues of inter-rater reliability and scalability. As part of the NSF funded SENSE-IT project to introduce secondary school science students to sensor networks (NSF Grant No. 0833440), semantic graphs illustrating how temperature change affects water ecology were collected from 221 students across 16 schools. The graphing task did not constrain students' use of terms, as is often done with semantic graph based assessment due to coding and scoring concerns. The graphing software used provided real-time feedback to help students learn how to construct graphs, stay on topic and effectively communicate ideas. The collected graphs were scored by human raters using assessment methods expected to boost reliability, which included adaptations of traditional holistic and propositional scoring methods, use of expert raters, topical rubrics, and criterion graphs. High levels of inter-rater reliability were achieved, demonstrating that vocabulary constraints may not be necessary after all. To investigate a new approach to automating the scoring of graphs, thirty-two different graph features characterizing graphs' structure, semantics, configuration and process of construction were then used to predict human raters' scoring of graphs in order to identify feature patterns correlated to raters' evaluations of graphs' topical accuracy and complexity. Results led to the development of a regression model able to predict raters' scoring with 77% accuracy, with 46% accuracy expected when used to score new sets of graphs, as estimated via cross-validation tests. Although such performance is comparable to other graph and essay based scoring systems, cross-context testing of the model and methods used to develop it would be needed before it could be recommended for widespread use. Still, the findings suggest techniques for improving the reliability and scalability of semantic graph based assessments without requiring constraint of how ideas are expressed.

Remapping Nominal Features in the Second Language

ERIC Educational Resources Information Center

Cho, Ji-Hyeon Jacee

2012-01-01

This dissertation investigates second language (L2) development in the domains of morphosyntax and semantics. Specifically, it examines the acquisition of definiteness and specificity in Russian within the Feature Re-assembly framework (Lardiere, 2009), according to which the hardest L2 learning task is not to reset parameters but to reconfigure,…

An Investigation of the Relationship between Grammar Type and Efficacy of Form-Focused Instruction

ERIC Educational Resources Information Center

Schenck, Andrew Douglas

2018-01-01

Because phonological, semantic, and morphosyntactic characteristics of grammatical features can have a significant impact on form-focused instruction, utilization of different grammatical features to test new language teaching techniques may conflate determinations of efficacy or inefficacy. The purpose of this study was to holistically examine…

New development of the image matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Feng, Zhao

2018-04-01

To study the image matching algorithm, algorithm four elements are described, i.e., similarity measurement, feature space, search space and search strategy. Four common indexes for evaluating the image matching algorithm are described, i.e., matching accuracy, matching efficiency, robustness and universality. Meanwhile, this paper describes the principle of image matching algorithm based on the gray value, image matching algorithm based on the feature, image matching algorithm based on the frequency domain analysis, image matching algorithm based on the neural network and image matching algorithm based on the semantic recognition, and analyzes their characteristics and latest research achievements. Finally, the development trend of image matching algorithm is discussed. This study is significant for the algorithm improvement, new algorithm design and algorithm selection in practice.

A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.

PubMed

Hasan, Mehedi; Kotov, Alexander; Carcone, April; Dong, Ming; Naar, Sylvie; Hartlieb, Kathryn Brogan

2016-08-01

This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of categories. We used a collection of motivational interview transcripts consisting of 11,353 utterances, which were manually annotated by two human coders as the gold standard, and experimented with state-of-art classifiers, including Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), Random Forest (RF), AdaBoost, DiscLDA, Conditional Random Fields (CRF) and Convolutional Neural Network (CNN) in conjunction with lexical, contextual (label of the previous utterance) and semantic (distribution of words in the utterance across the Linguistic Inquiry and Word Count dictionaries) features. We found out that, when the number of classes is large, the performance of CNN and CRF is inferior to SVM. When only lexical features were used, interview transcripts were automatically annotated by SVM with the highest classification accuracy among all classifiers of 70.8%, 61% and 53.7% based on the codebooks consisting of 17, 20 and 41 codes, respectively. Using contextual and semantic features, as well as their combination, in addition to lexical ones, improved the accuracy of SVM for annotation of utterances in motivational interview transcripts with a codebook consisting of 17 classes to 71.5%, 74.2%, and 75.1%, respectively. Our results demonstrate the potential of using machine learning methods in conjunction with lexical, semantic and contextual features for automatic annotation of clinical interview transcripts with near-human accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

Laban Movement Analysis towards Behavior Patterns

NASA Astrophysics Data System (ADS)

Santos, Luís; Dias, Jorge

This work presents a study about the use of Laban Movement Analysis (LMA) as a robust tool to describe human basic behavior patterns, to be applied in human-machine interaction. LMA is a language used to describe and annotate dancing movements and is divided in components [1]: Body, Space, Shape and Effort. Despite its general framework is widely used in physical and mental therapy [2], it has found little application in the engineering domain. Rett J. [3] proposed to implement LMA using Bayesian Networks. However LMA component models have not yet been fully implemented. A study on how to approach behavior using LMA is presented. Behavior is a complex feature and movement chain, but we believe that most basic behavior primitives can be discretized in simple features. Correctly identifying Laban parameters and the movements the authors feel that good patterns can be found within a specific set of basic behavior semantics.

Structural and functional integration between dorsal and ventral language streams as revealed by blunt dissection and direct electrical stimulation.

PubMed

Sarubbo, Silvio; De Benedictis, Alessandro; Merler, Stefano; Mandonnet, Emmanuel; Barbareschi, Mattia; Dallabona, Monica; Chioffi, Franco; Duffau, Hugues

2016-11-01

The most accepted framework of language processing includes a dorsal phonological and a ventral semantic pathway, connecting a wide network of distributed cortical hubs. However, the cortico-subcortical connectivity and the reciprocal anatomical relationships of this dual-stream system are not completely clarified. We performed an original blunt microdissection of 10 hemispheres with the exposition of locoregional short fibers and six long-range fascicles involved in language elaboration. Special attention was addressed to the analysis of termination sites and anatomical relationships between long- and short-range fascicles. We correlated these anatomical findings with a topographical analysis of 93 functional responses located at the terminal sites of the language bundles, collected by direct electrical stimulation in 108 right-handers. The locations of phonological and semantic paraphasias, verbal apraxia, speech arrest, pure anomia, and alexia were statistically analyzed, and the respective barycenters were computed in the MNI space. We found that terminations of main language bundles and functional responses have a wider distribution in respect to the classical definition of language territories. Our analysis showed that dorsal and ventral streams have a similar anatomical layer organization. These pathways are parallel and relatively segregated over their subcortical course while their terminal fibers are strictly overlapped at the cortical level. Finally, the anatomical features of the U-fibers suggested a role of locoregional integration between the phonological, semantic, and executive subnetworks of language, in particular within the inferoventral frontal lobe and the temporoparietal junction, which revealed to be the main criss-cross regions between the dorsal and ventral pathways. Hum Brain Mapp 37:3858-3872, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The relationships between the amount of spared tissue, percent signal change, and accuracy in semantic processing in aphasia.

PubMed

Sims, Jordyn A; Kapse, Kushal; Glynn, Peter; Sandberg, Chaleece; Tripodis, Yorghos; Kiran, Swathi

2016-04-01

Recovery from aphasia, loss of language following a cerebrovascular incident (stroke), is a complex process involving both left and right hemispheric regions. In our study, we analyzed the relationships between semantic processing behavioral data, lesion size and location, and percent signal change from functional magnetic resonance imaging (fMRI) data. This study included 14 persons with aphasia in the chronic stage of recovery (six or more months post stroke), along with normal controls, who performed semantic processing tasks of determining whether a written semantic feature matched a picture or whether two written words were related. Using region of interest (ROI) analysis, we found that left inferior frontal gyrus pars opercularis and pars triangularis, despite significant damage, were the only regions to correlate with behavioral accuracy. Additionally, bilateral frontal regions including superior frontal gyrus, middle frontal gyrus, and anterior cingulate appear to serve as an assistive network in the case of damage to traditional language regions that include inferior frontal gyrus, middle temporal gyrus, supramarginal gyrus, and angular gyrus. Right hemisphere posterior regions including right middle temporal gyrus, right supramarginal gyrus, and right angular gyrus are engaged in the case of extensive damage to left hemisphere language regions. Additionally, right inferior frontal gyrus pars orbitalis is presumed to serve a monitoring function. These results reinforce the importance of the left hemisphere in language processing in aphasia, and provide a framework for the relative importance of left and right language regions in the brain. Copyright © 2016 Elsevier Ltd. All rights reserved.

Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence

PubMed Central

Neyens, Veerle; Bruffaerts, Rose; Liuzzi, Antonietta G.; Kalfas, Ioannis; Peeters, Ronald; Keuleers, Emmanuel; Vogels, Rufin; De Deyne, Simon; Storms, Gert; Dupont, Patrick; Vandenberghe, Rik

2017-01-01

According to a recent study, semantic similarity between concrete entities correlates with the similarity of activity patterns in left middle IPS during category naming. We examined the replicability of this effect under passive viewing conditions, the potential role of visuoperceptual similarity, where the effect is situated compared to regions that have been previously implicated in visuospatial attention, and how it compares to effects of object identity and location. Forty-six subjects participated. Subjects passively viewed pictures from two categories, musical instruments and vehicles. Semantic similarity between entities was estimated based on a concept-feature matrix obtained in more than 1,000 subjects. Visuoperceptual similarity was modeled based on the HMAX model, the AlexNet deep convolutional learning model, and thirdly, based on subjective visuoperceptual similarity ratings. Among the IPS regions examined, only left middle IPS showed a semantic similarity effect. The effect was significant in hIP1, hIP2, and hIP3. Visuoperceptual similarity did not correlate with similarity of activity patterns in left middle IPS. The semantic similarity effect in left middle IPS was significantly stronger than in the right middle IPS and also stronger than in the left or right posterior IPS. The semantic similarity effect was similar to that seen in the angular gyrus. Object identity effects were much more widespread across nearly all parietal areas examined. Location effects were relatively specific for posterior IPS and area 7 bilaterally. To conclude, the current findings replicate the semantic similarity effect in left middle IPS under passive viewing conditions, and demonstrate its anatomical specificity within a cytoarchitectonic reference frame. We propose that the semantic similarity effect in left middle IPS reflects the transient uploading of semantic representations in working memory. PMID:28824405

Computerized Analysis of Verbal Fluency: Normative Data and the Effects of Repeated Testing, Simulated Malingering, and Traumatic Brain Injury

PubMed Central

Wyma, John M.; Herron, Timothy J.; Yund, E. William

2016-01-01

In verbal fluency (VF) tests, subjects articulate words in a specified category during a short test period (typically 60 s). Verbal fluency tests are widely used to study language development and to evaluate memory retrieval in neuropsychiatric disorders. Performance is usually measured as the total number of correct words retrieved. Here, we describe the properties of a computerized VF (C-VF) test that tallies correct words and repetitions while providing additional lexical measures of word frequency, syllable count, and typicality. In addition, the C-VF permits (1) the analysis of the rate of responding over time, and (2) the analysis of the semantic relationships between words using a new method, Explicit Semantic Analysis (ESA), as well as the established semantic clustering and switching measures developed by Troyer et al. (1997). In Experiment 1, we gathered normative data from 180 subjects ranging in age from 18 to 82 years in semantic (“animals”) and phonemic (letter “F”) conditions. The number of words retrieved in 90 s correlated with education and daily hours of computer-use. The rate of word production declined sharply over time during both tests. In semantic conditions, correct-word scores correlated strongly with the number of ESA and Troyer-defined semantic switches as well as with an ESA-defined semantic organization index (SOI). In phonemic conditions, ESA revealed significant semantic influences in the sequence of words retrieved. In Experiment 2, we examined the test-retest reliability of different measures across three weekly tests in 40 young subjects. Different categories were used for each semantic (“animals”, “parts of the body”, and “foods”) and phonemic (letters “F”, “A”, and “S”) condition. After regressing out the influences of education and computer-use, we found that correct-word z-scores in the first session did not differ from those of the subjects in Experiment 1. Word production was uniformly greater in semantic than phonemic conditions. Intraclass correlation coefficients (ICCs) of correct-word z-scores were higher for phonemic (0.91) than semantic (0.77) tests. In semantic conditions, good reliability was also seen for the SOI (ICC = 0.68) and ESA-defined switches in semantic categories (ICC = 0.62). In Experiment 3, we examined the performance of subjects from Experiment 2 when instructed to malinger: 38% showed abnormal (p< 0.05) performance in semantic conditions. Simulated malingerers with abnormal scores could be distinguished with 80% sensitivity and 89% specificity from subjects with abnormal scores in Experiment 1 using lexical, temporal, and semantic measures. In Experiment 4, we tested patients with mild and severe traumatic brain injury (mTBI and sTBI). Patients with mTBI performed within the normal range, while patients with sTBI showed significant impairments in correct-word z-scores and category shifts. The lexical, temporal, and semantic measures of the C-VF provide an automated and comprehensive description of verbal fluency performance. PMID:27936001

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

PubMed

El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M

2015-11-01

Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.

Organizing Diverse, Distributed Project Information

NASA Technical Reports Server (NTRS)

Keller, Richard M.

2003-01-01

SemanticOrganizer is a software application designed to organize and integrate information generated within a distributed organization or as part of a project that involves multiple, geographically dispersed collaborators. SemanticOrganizer incorporates the capabilities of database storage, document sharing, hypermedia navigation, and semantic-interlinking into a system that can be customized to satisfy the specific information-management needs of different user communities. The program provides a centralized repository of information that is both secure and accessible to project collaborators via the World Wide Web. SemanticOrganizer's repository can be used to collect diverse information (including forms, documents, notes, data, spreadsheets, images, and sounds) from computers at collaborators work sites. The program organizes the information using a unique network-structured conceptual framework, wherein each node represents a data record that contains not only the original information but also metadata (in effect, standardized data that characterize the information). Links among nodes express semantic relationships among the data records. The program features a Web interface through which users enter, interlink, and/or search for information in the repository. By use of this repository, the collaborators have immediate access to the most recent project information, as well as to archived information. A key advantage to SemanticOrganizer is its ability to interlink information together in a natural fashion using customized terminology and concepts that are familiar to a user community.

Developmental amnesia: Fractionation of developing memory systems.

PubMed

Temple, Christine M; Richardson, Paul

2006-07-01

Study of the developmental amnesias utilizing a cognitive neuropsychological methodology has highlighted the dissociations that may occur between the development of components of memory. M.M., a new case of developmental amnesia, was identified after screening from the normal population on cognitive and memory measures. Retrospective investigation found that he was of low birthweight. M.M. had impaired semantic memory for knowledge of facts and words. There was impaired episodic memory for words and stories but intact episodic memory for visual designs and features. This forms a double dissociation with Dr S. (Temple, 1992), who had intact verbal but impaired visual episodic memory. M.M. also had impaired autobiographical episodic memory. Nevertheless, learning over repeated trials occurred, consistent with previous theorizing that learning is not simply the effect of recurrent episodic memory. Nor is it the same as establishing semantic memory, since for M.M. semantic memory is also impaired. Within reading, there was an impaired lexico-semantic system, elevated levels of homophone confusion, but intact phonological reading, consistent with surface dyslexia and raising issues about the interrelationship of the semantic system and literacy development. The results are compatible with discrete semi-independent components within memory development, whereby deficits are associated with residual normality, but there may also be an explicit relationship between the semantic memory system and both vocabulary and reading acquisition.

The elephant in the room: Inconsistency in scene viewing and representation.

PubMed

Spotorno, Sara; Tatler, Benjamin W

2017-10-01

We examined the extent to which semantic informativeness, consistency with expectations and perceptual salience contribute to object prioritization in scene viewing and representation. In scene viewing (Experiments 1-2), semantic guidance overshadowed perceptual guidance in determining fixation order, with the greatest prioritization for objects that were diagnostic of the scene's depicted event. Perceptual properties affected selection of consistent objects (regardless of their informativeness) but not of inconsistent objects. Semantic and perceptual properties also interacted in influencing foveal inspection, as inconsistent objects were fixated longer than low but not high salience diagnostic objects. While not studied in direct competition with each other (each studied in competition with diagnostic objects), we found that inconsistent objects were fixated earlier and for longer than consistent but marginally informative objects. In change detection (Experiment 3), perceptual guidance overshadowed semantic guidance, promoting detection of highly salient changes. A residual advantage for diagnosticity over inconsistency emerged only when selection prioritization could not be based on low-level features. Overall these findings show that semantic inconsistency is not prioritized within a scene when competing with other relevant information that is essential to scene understanding and respects observers' expectations. Moreover, they reveal that the relative dominance of semantic or perceptual properties during selection depends on ongoing task requirements. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

How Necessary Are the Stripes of a Tiger? Diagnostic and Characteristic Features in an fMRI Study of Word Meaning

ERIC Educational Resources Information Center

Grossman, Murray; Troiani, Vanessa; Koenig, Phyllis; Work, Melissa; Moore, Peachie

2007-01-01

This study contrasted two approaches to word meaning: the statistically determined role of high-contribution features like "striped" in the meaning of complex nouns like "tiger" typically used in studies of semantic memory, and the contribution of diagnostic features like "parent's brother" that play a critical role in the meaning of nominal kinds…

An RT distribution analysis of relatedness proportion effects in lexical decision and semantic categorization reveals different mechanisms.

PubMed

de Wit, Bianca; Kinoshita, Sachiko

2015-01-01

The magnitude of the semantic priming effect is known to increase as the proportion of related prime-target pairs in an experiment increases. This relatedness proportion (RP) effect was studied in a lexical decision task at a short prime-target stimulus onset asynchrony (240 ms), which is widely assumed to preclude strategic prospective usage of the prime. The analysis of the reaction time (RT) distribution suggested that the observed RP effect reflected a modulation of a retrospective semantic matching process. The pattern of the RP effect on the RT distribution found here is contrasted to that reported in De Wit and Kinoshita's (2014) semantic categorization study, and it is concluded that the RP effect is driven by different underlying mechanisms in lexical decision and semantic categorization.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

PubMed Central

2012-01-01

Background The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. Results The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Conclusions Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net. PMID:22889332

Avogadro: an advanced semantic chemical editor, visualization, and analysis platform.

PubMed

Hanwell, Marcus D; Curtis, Donald E; Lonie, David C; Vandermeersch, Tim; Zurek, Eva; Hutchison, Geoffrey R

2012-08-13

The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.

The contribution of the left anterior ventrolateral temporal lobe to the retrieval of personal semantics.

PubMed

Grilli, Matthew D; Bercel, John J; Wank, Aubrey A; Rapcsak, Steven Z

2018-06-04

Autobiographical facts and personal trait knowledge are conceptualized as distinct types of personal semantics, but the cognitive and neural mechanisms that separate them remain underspecified. One distinction may be their level of specificity, with autobiographical facts reflecting idiosyncratic conceptual knowledge and personal traits representing basic level category knowledge about the self. Given the critical role of the left anterior ventrolateral temporal lobe (AVTL) in the storage and retrieval of semantic information about unique entities, we hypothesized that knowledge of autobiographical facts may depend on the integrity of this region to a greater extent than personal traits. To provide neuropsychological evidence relevant to this issue, we investigated personal semantics, semantic knowledge of non-personal unique entities, and episodic memory in two individuals with well-defined left (MK) versus right (DW) AVTL lesions. Relative to controls, MK demonstrated preserved personal trait knowledge but impaired "experience-far" (i.e., spatiotemporal independent) autobiographical fact knowledge, semantic memory for non-personal unique entities, and episodic memory. In contrast, both experience-far autobiographical facts and personal traits were spared in DW, whereas episodic memory and aspects of semantic memory for non-personal unique entities were impaired. These findings support the notion that autobiographical facts and personal traits have distinct cognitive features and neural mechanisms. They also suggest a common organizing principle for personal and non-personal semantics, namely the specificity of such knowledge to an entity, which is reflected in the contribution of the left AVTL to retrieval. Copyright © 2018 Elsevier Ltd. All rights reserved.

An adaptive semantic matching paradigm for reliable and valid language mapping in individuals with aphasia.

PubMed

Wilson, Stephen M; Yen, Melodie; Eriksson, Dana K

2018-04-17

Research on neuroplasticity in recovery from aphasia depends on the ability to identify language areas of the brain in individuals with aphasia. However, tasks commonly used to engage language processing in people with aphasia, such as narrative comprehension and picture naming, are limited in terms of reliability (test-retest reproducibility) and validity (identification of language regions, and not other regions). On the other hand, paradigms such as semantic decision that are effective in identifying language regions in people without aphasia can be prohibitively challenging for people with aphasia. This paper describes a new semantic matching paradigm that uses an adaptive staircase procedure to present individuals with stimuli that are challenging yet within their competence, so that language processing can be fully engaged in people with and without language impairments. The feasibility, reliability and validity of the adaptive semantic matching paradigm were investigated in sixteen individuals with chronic post-stroke aphasia and fourteen neurologically normal participants, in comparison to narrative comprehension and picture naming paradigms. All participants succeeded in learning and performing the semantic paradigm. Test-retest reproducibility of the semantic paradigm in people with aphasia was good (Dice coefficient = 0.66), and was superior to the other two paradigms. The semantic paradigm revealed known features of typical language organization (lateralization; frontal and temporal regions) more consistently in neurologically normal individuals than the other two paradigms, constituting evidence for validity. In sum, the adaptive semantic matching paradigm is a feasible, reliable and valid method for mapping language regions in people with aphasia. © 2018 Wiley Periodicals, Inc.

Re-examination of Chinese semantic processing and syntactic processing: evidence from conventional ERPs and reconstructed ERPs by residue iteration decomposition (RIDE).

PubMed

Wang, Fang; Ouyang, Guang; Zhou, Changsong; Wang, Suiping

2015-01-01

A number of studies have explored the time course of Chinese semantic and syntactic processing. However, whether syntactic processing occurs earlier than semantics during Chinese sentence reading is still under debate. To further explore this issue, an event-related potentials (ERPs) experiment was conducted on 21 native Chinese speakers who read individually-presented Chinese simple sentences (NP1+VP+NP2) word-by-word for comprehension and made semantic plausibility judgments. The transitivity of the verbs was manipulated to form three types of stimuli: congruent sentences (CON), sentences with a semantically violated NP2 following a transitive verb (semantic violation, SEM), and sentences with a semantically violated NP2 following an intransitive verb (combined semantic and syntactic violation, SEM+SYN). The ERPs evoked from the target NP2 were analyzed by using the Residue Iteration Decomposition (RIDE) method to reconstruct the ERP waveform blurred by trial-to-trial variability, as well as by using the conventional ERP method based on stimulus-locked averaging. The conventional ERP analysis showed that, compared with the critical words in CON, those in SEM and SEM+SYN elicited an N400-P600 biphasic pattern. The N400 effects in both violation conditions were of similar size and distribution, but the P600 in SEM+SYN was bigger than that in SEM. Compared with the conventional ERP analysis, RIDE analysis revealed a larger N400 effect and an earlier P600 effect (in the time window of 500-800 ms instead of 570-810ms). Overall, the combination of conventional ERP analysis and the RIDE method for compensating for trial-to-trial variability confirmed the non-significant difference between SEM and SEM+SYN in the earlier N400 time window. Converging with previous findings on other Chinese structures, the current study provides further precise evidence that syntactic processing in Chinese does not occur earlier than semantic processing.

Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer's Disease. A Bayesian Network Model based on the Analysis of Oral Definitions of Semantic Categories.

PubMed

Guerrero, J M; Martínez-Tomás, R; Rincón, M; Peraita, H

2016-01-01

Early detection of Alzheimer's disease (AD) has become one of the principal focuses of research in medicine, particularly when the disease is incipient or even prodromic, because treatments are more effective in these stages. Lexical-semantic-conceptual deficit (LSCD) in the oral definitions of semantic categories for basic objects is an important early indicator in the evaluation of the cognitive state of patients. The objective of this research is to define an economic procedure for cognitive impairment (CI) diagnosis, which may be associated with early stages of AD, by analysing cognitive alterations affecting declarative semantic memory. Because of its low cost, it could be used for routine clinical evaluations or screenings, leading to more expensive and selective tests that confirm or rule out the disease accurately. It should necessarily be an explanatory procedure, which would allow us to study the evolution of the disease in relation to CI, the irregularities in different semantic categories, and other neurodegenerative diseases. On the basis of these requirements, we hypothesise that Bayesian networks (BNs) are the most appropriate tool for this purpose. We have developed a BN for CI diagnosis in mild and moderate AD patients by analysing the oral production of semantic features. The BN causal model represents LSCD in certain semantic categories, both of living things (dog, pine, and apple) and non-living things (chair, car, and trousers), as symptoms of CI. The model structure, the qualitative part of the model, uses domain knowledge obtained from psychology experts and epidemiological studies. Further, the model parameters, the quantitative part of the model, are learnt automatically from epidemiological studies and Peraita and Grasso's linguistic corpus of oral definitions. This corpus was prepared with an incidental sampling and included the analysis of the oral linguistic production of 81 participants (42 cognitively healthy elderly people and 39 mild and moderate AD patients) from Madrid region's hospitals. Experienced neurologists diagnosed these cases following the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA)'s Alzheimer's criteria, performing, among other explorations and tests, a minimum neuropsychological exploration that included the Mini-Mental State Examination test. BN's classification performance is remarkable compared with other machine learning methods, achieving 91% accuracy and 94% precision in mild and moderate AD patients. Apart from this, the BN model facilitates the explanation of the reasoning process and the validation of the conclusions and allows the study of uncommon declarative semantic memory impairments. Our method is able to analyse LSCD in a wide set of semantic categories throughout the progression of CI, being a valuable first screening method in AD diagnosis in its early stages. Because of its low cost, it can be used for routine clinical evaluations or screenings to detect AD in its early stages. Besides, due to its knowledge-based structure, it can be easily extended to provide an explanation of the diagnosis and to the study of other neurodegenerative diseases. Further, this is a key advantage of BNs over other machine learning methods with similar performance: it is a recognisable and explanatory model that allows one to study irregularities in different semantic categories.

Analysis of a mammography teaching program based on an affordance design model.

PubMed

Luo, Ping; Eikman, Edward A; Kealy, William; Qian, Wei

2006-12-01

The wide use of computer technology in education, particularly in mammogram reading, asks for e-learning evaluation. The existing media comparative studies, learner attitude evaluations, and performance tests are problematic. Based on an affordance design model, this study examined an existing e-learning program on mammogram reading. The selection criteria include content relatedness, representativeness, e-learning orientation, image quality, program completeness, and accessibility. A case study was conducted to examine the affordance features, functions, and presentations of the selected software. Data collection and analysis methods include interviews, protocol-based document analysis, and usability tests and inspection. Also some statistics were calculated. The examination of PBE identified that this educational software designed and programmed some tools. The learner can use these tools in the process of optimizing displays, scanning images, comparing different projections, marking the region of interests, constructing a descriptive report, assessing one's learning outcomes, and comparing one's decisions with the experts' decisions. Further, PBE provides some resources for the learner to construct one's knowledge and skills, including a categorized image library, a term-searching function, and some teaching links. Besides, users found it easy to navigate and carry out tasks. The users also reacted positively toward PBE's navigation system, instructional aids, layout, pace and flow of information, graphics, and other presentation design. The software provides learners with some cognitive tools, supporting their perceptual problem-solving processes and extending their capabilities. Learners can internalize the mental models in mammogram reading through multiple perceptual triangulations, sensitization of related features, semantic description of mammogram findings, and expert-guided semantic report construction. The design of these cognitive tools and the software interface matches the findings and principles in human learning and instructional design. Working with PBE's case-based simulations and categorized gallery, learners can enrich and transfer their experience to their jobs.

Progress in The Semantic Analysis of Scientific Code

NASA Technical Reports Server (NTRS)

Stewart, Mark

2000-01-01

This paper concerns a procedure that analyzes aspects of the meaning or semantics of scientific and engineering code. This procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and parsing this annotated code using multiple, independent expert parsers. These semantic parsers encode domain knowledge and recognize formulae in different disciplines including physics, numerical methods, mathematics, and geometry. The parsers will automatically recognize and document some static, semantic concepts and help locate some program semantic errors. These techniques may apply to a wider range of scientific codes. If so, the techniques could reduce the time, risk, and effort required to develop and modify scientific codes.

From bird to sparrow: Learning-induced modulations in fine-grained semantic discrimination.

PubMed

De Meo, Rosanna; Bourquin, Nathalie M-P; Knebel, Jean-François; Murray, Micah M; Clarke, Stephanie

2015-09-01

Recognition of environmental sounds is believed to proceed through discrimination steps from broad to more narrow categories. Very little is known about the neural processes that underlie fine-grained discrimination within narrow categories or about their plasticity in relation to newly acquired expertise. We investigated how the cortical representation of birdsongs is modulated by brief training to recognize individual species. During a 60-minute session, participants learned to recognize a set of birdsongs; they improved significantly their performance for trained (T) but not control species (C), which were counterbalanced across participants. Auditory evoked potentials (AEPs) were recorded during pre- and post-training sessions. Pre vs. post changes in AEPs were significantly different between T and C i) at 206-232ms post stimulus onset within a cluster on the anterior part of the left superior temporal gyrus; ii) at 246-291ms in the left middle frontal gyrus; and iii) 512-545ms in the left middle temporal gyrus as well as bilaterally in the cingulate cortex. All effects were driven by weaker activity for T than C species. Thus, expertise in discriminating T species modulated early stages of semantic processing, during and immediately after the time window that sustains the discrimination between human vs. animal vocalizations. Moreover, the training-induced plasticity is reflected by the sharpening of a left lateralized semantic network, including the anterior part of the temporal convexity and the frontal cortex. Training to identify birdsongs influenced, however, also the processing of C species, but at a much later stage. Correct discrimination of untrained sounds seems to require an additional step which results from lower-level features analysis such as apperception. We therefore suggest that the access to objects within an auditory semantic category is different and depends on subject's level of expertise. More specifically, correct intra-categorical auditory discrimination for untrained items follows the temporal hierarchy and transpires in a late stage of semantic processing. On the other hand, correct categorization of individually trained stimuli occurs earlier, during a period contemporaneous with human vs. animal vocalization discrimination, and involves a parallel semantic pathway requiring expertise. Copyright © 2015 Elsevier Inc. All rights reserved.

ER2OWL: Generating OWL Ontology from ER Diagram

NASA Astrophysics Data System (ADS)

Fahad, Muhammad

Ontology is the fundamental part of Semantic Web. The goal of W3C is to bring the web into (its full potential) a semantic web with reusing previous systems and artifacts. Most legacy systems have been documented in structural analysis and structured design (SASD), especially in simple or Extended ER Diagram (ERD). Such systems need up-gradation to become the part of semantic web. In this paper, we present ERD to OWL-DL ontology transformation rules at concrete level. These rules facilitate an easy and understandable transformation from ERD to OWL. The set of rules for transformation is tested on a structured analysis and design example. The framework provides OWL ontology for semantic web fundamental. This framework helps software engineers in upgrading the structured analysis and design artifact ERD, to components of semantic web. Moreover our transformation tool, ER2OWL, reduces the cost and time for building OWL ontologies with the reuse of existing entity relationship models.

Tracking Real-Time Neural Activation of Conceptual Knowledge Using Single-Trial Event-Related Potentials

ERIC Educational Resources Information Center

Amsel, Ben D.

2011-01-01

Empirically derived semantic feature norms categorized into different types of knowledge (e.g., visual, functional, auditory) can be summed to create number-of-feature counts per knowledge type. Initial evidence suggests several such knowledge types may be recruited during language comprehension. The present study provides a more detailed…

Correlated Topic Vector for Scene Classification.

PubMed

Wei, Pengxu; Qin, Fei; Wan, Fang; Zhu, Yi; Jiao, Jianbin; Ye, Qixiang

2017-07-01

Scene images usually involve semantic correlations, particularly when considering large-scale image data sets. This paper proposes a novel generative image representation, correlated topic vector, to model such semantic correlations. Oriented from the correlated topic model, correlated topic vector intends to naturally utilize the correlations among topics, which are seldom considered in the conventional feature encoding, e.g., Fisher vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy. Incorporated with the Fisher kernel method, correlated topic vector inherits the advantages of Fisher vector. The contributions to the topics of visual words have been further employed by incorporating the Fisher kernel framework to indicate the differences among scenes. Combined with the deep convolutional neural network (CNN) features and Gibbs sampling solution, correlated topic vector shows great potential when processing large-scale and complex scene image data sets. Experiments on two scene image data sets demonstrate that correlated topic vector improves significantly the deep CNN features, and outperforms existing Fisher kernel-based features.

Integrating conceptual knowledge within and across representational modalities.

PubMed

McNorgan, Chris; Reid, Jackie; McRae, Ken

2011-02-01

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants' knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual-feature verification task. The pattern of decision latencies across Experiments 1-4 is consistent with a deep integration hierarchy. Copyright © 2010 Elsevier B.V. All rights reserved.

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients

PubMed Central

Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J.; Schabath, Matthew B.

2017-01-01

The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features. PMID:29221183

Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics.

PubMed

Rajabi, Enayat; Abidi, Syed Sibte Raza

2017-01-01

The aim of this paper is to investigate semantic web based methods to enrich and transform a medical discussion forum in order to perform semantics-driven social network analysis. We use the centrality measures as well as semantic similarity metrics to identify the most influential practitioners within a discussion forum. The centrality results of our approach are in line with centrality measures produced by traditional SNA methods, thus validating the applicability of semantic web based methods for SNA, particularly for analyzing social networks for specialized discussion forums.

Syntactic processing as a marker for cognitive impairment in amyotrophic lateral sclerosis

PubMed Central

Tsermentseli, Stella; Leigh, P. Nigel; Taylor, Lorna J.; Radunovic, Aleksandar; Catani, Marco; Goldstein, Laura H.

2016-01-01

Despite recent interest in cognitive changes in patients with amyotrophic lateral sclerosis (ALS), investigations of language function looking at the level of word, sentence and discourse processing are relatively scarce. Data were obtained from 26 patients with sporadic ALS and 26 healthy controls matched for age, education, gender, anxiety, depression and executive function performance. Standardized language tasks included confrontation naming, semantic access, and syntactic comprehension. Quantitative production analysis (QPA) was used to analyse connected speech samples of the Cookie Theft picture description task. Results showed that the ALS patients were impaired on standardized measures of grammatical comprehension and action/verb semantics. At the level of discourse, ALS patients were impaired on measures of syntactic complexity and fluency; however, the latter could be better explained by disease related factors. Discriminant analysis revealed that syntactic measures differentiated ALS patients from controls. In conclusion, patients with ALS exhibit deficits in receptive and expressive language on tasks of comprehension and connected speech production, respectively. Our findings suggest that syntactic processing deficits seem to be the predominant feature of language impairment in ALS and that these deficits can be detected by relatively simple language tests. PMID:26312952

Syntactic processing as a marker for cognitive impairment in amyotrophic lateral sclerosis.

PubMed

Tsermentseli, Stella; Leigh, P Nigel; Taylor, Lorna J; Radunovic, Aleksandar; Catani, Marco; Goldstein, Laura H

2015-01-01

Despite recent interest in cognitive changes in patients with amyotrophic lateral sclerosis (ALS), investigations of language function looking at the level of word, sentence and discourse processing are relatively scarce. Data were obtained from 26 patients with sporadic ALS and 26 healthy controls matched for age, education, gender, anxiety, depression and executive function performance. Standardized language tasks included confrontation naming, semantic access, and syntactic comprehension. Quantitative production analysis (QPA) was used to analyse connected speech samples of the Cookie Theft picture description task. Results showed that the ALS patients were impaired on standardized measures of grammatical comprehension and action/verb semantics. At the level of discourse, ALS patients were impaired on measures of syntactic complexity and fluency; however, the latter could be better explained by disease related factors. Discriminant analysis revealed that syntactic measures differentiated ALS patients from controls. In conclusion, patients with ALS exhibit deficits in receptive and expressive language on tasks of comprehension and connected speech production, respectively. Our findings suggest that syntactic processing deficits seem to be the predominant feature of language impairment in ALS and that these deficits can be detected by relatively simple language tests.

Hemispheric differences in the recruitment of semantic processing mechanisms

PubMed Central

Kandhadai, Padmapriya; Federmeier, Kara D.

2010-01-01

This study examined how the two cerebral hemispheres recruit semantic processing mechanisms by combining event-related potential measures and visual half-field methods in a word priming paradigm in which semantic strength and predictability were manipulated using lexically associated word pairs. Activation patterns on the Late Positive Complex (LPC), linked to controlled aspects of processing, showed that previously documented left hemisphere (LH) processing benefits for word pairs with a weak forward but strong backward association stem from the ability to appreciate meaning relations in an order-independent fashion and/or strategically reorder them. Whereas there is a LH benefit for such strategic processing during comprehension in passive tasks, the present study further showed that the RH is also able to make use of these mechanisms when explicit semantic judgments are required. In both hemispheres, N400 responses, linked to initial semantic activation, were largely graded by association strength, with more amplitude reduction for forward associates and strong, symmetrically associated pairs compared to backward associates and matched weak, symmetrically associated pairs. However, responses to moderately associated pairs were more facilitated after initial presentation to the LH than to the RH. This pattern converges with sentence processing findings that point to LH advantages for using context information to predict features of likely upcoming words. Together, the results suggest that an important basis for hemispheric asymmetries in language comprehension arises from when and how each uses top-down semantic mechanisms to shape initial semantic activation over time. PMID:20638397

Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

NASA Astrophysics Data System (ADS)

Fox, P.

2012-04-01

The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

Lexical Processing in Toddlers with ASD: Does Weak Central Coherence Play a Role?

PubMed Central

Weismer, Susan Ellis; Haebig, Eileen; Edwards, Jan; Saffran, Jenny; Venker, Courtney E.

2016-01-01

This study investigated whether vocabulary delays in toddlers with autism spectrum disorders (ASD) can be explained by a cognitive style that prioritizes processing of detailed, local features of input over global contextual integration – as claimed by the weak central coherence (WCC) theory. Thirty toddlers with ASD and 30 younger, cognition-matched typical controls participated in a looking-while-listening task that assessed whether perceptual or semantic similarities among named images disrupted word recognition relative to a neutral condition. Overlap of perceptual features invited local processing whereas semantic overlap invited global processing. With the possible exception of a subset of toddlers who had very low vocabulary skills, these results provide no evidence that WCC is characteristic of lexical processing in toddlers with ASD. PMID:27696177

Classification of Aerial Photogrammetric 3d Point Clouds

NASA Astrophysics Data System (ADS)

Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

2017-05-01

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

Disruption of Semantic Network in Mild Alzheimer’s Disease Revealed by Resting-State fMRI

PubMed Central

Mascali, Daniele; DiNuzzo, Mauro; Serra, Laura; Mangia, Silvia; Maraviglia, Bruno; Bozzali, Marco; Giove, Federico

2018-01-01

Subtle semantic deficits can be observed in Alzheimer’s disease (AD) patients even in the early stages of the illness. In this work, we tested the hypothesis that the semantic control network is deregulated in mild AD patients. We assessed the integrity of the semantic control system using resting-state functional magnetic resonance imaging in a cohort of patients with mild AD (n = 38; mean mini-mental state examination = 20.5) and in a group of age-matched healthy controls (n = 19). Voxel-wise analysis spatially constrained in the left fronto-temporal semantic control network identified two regions with altered functional connectivity (FC) in AD patients, specifically in the pars opercularis (POp, BA44) and in the posterior middle temporal gyrus (pMTG, BA21). Using whole-brain seed-based analysis, we demonstrated that these two regions have altered FC even beyond the semantic control network. In particular, the pMTG displayed a wide-distributed pattern of lower connectivity to several brain regions involved in language-semantic processing, along with a possibly compensatory higher connectivity to the Wernicke’s area. We conclude that in mild AD brain regions belonging to the semantic control network are abnormally connected not only within the network, but also to other areas known to be critical for language processing. PMID:29197559

Coarse coding and discourse comprehension in adults with right hemisphere brain damage

PubMed Central

Tompkins, Connie A.; Scharp, Victoria L.; Meigh, Kimberly M.; Fassbinder, Wiltrud

2009-01-01

Background Various investigators suggest that some discourse-level comprehension difficulties in adults with right hemisphere brain damage (RHD) have a lexical-semantic basis. As words are processed, the intact right hemisphere arouses and sustains activation of a wide-ranging network of secondary or peripheral meanings and features—a phenomenon dubbed “coarse coding”. Coarse coding impairment has been postulated to underpin some prototypical RHD comprehension deficits, such as difficulties with nonliteral language interpretation, discourse integration, some kinds of inference generation, and recovery when a reinterpretation is needed. To date, however, no studies have addressed the hypothesised link between coarse coding deficit and discourse comprehension in RHD. Aims The current investigation examined whether coarse coding was related to performance on two measures of narrative comprehension in adults with RHD. Methods & Procedures Participants were 32 adults with unilateral RHD from cerebrovascular accident, and 38 adults without brain damage. Coarse coding was operationalised as poor activation of peripheral/weakly related semantic features of words. For the coarse coding assessment, participants listened to spoken sentences that ended in a concrete noun. Each sentence was followed by a spoken target phoneme string. Targets were subordinate semantic features of the sentence-final nouns that were incompatible with their dominant mental representations (e.g., “rotten” for apple). Targets were presented at two post-noun intervals. A lexical decision task was used to gauge both early activation and maintenance of activation of these weakly related semantic features. One of the narrative tasks assessed comprehension of implied main ideas and details, while the other indexed high-level inferencing and integration. Both comprehension tasks were presented auditorily. For all tasks, accuracy of performance was the dependent measure. Correlations were computed within the RHD group between both the early and late coarse coding measures and the two discourse measures. Additionally, ANCOVA and independent t-tests were used to compare both early and sustained coarse coding in subgroups of good and poor RHD comprehenders. Outcomes & Results The group with RHD was less accurate than the control group on all measures. The finding of coarse coding impairment (difficulty activating/sustaining activation of a word’s peripheral features) may appear to contradict prior evidence of RHD suppression deficit (prolonged activation for context-inappropriate meanings of words). However, the sentence contexts in this study were unbiased and thus did not provide an appropriate test of suppression function. Correlations between coarse coding and the discourse measures were small and nonsignificant. There were no differences in coarse coding between RHD comprehension subgroups on the high-level inferencing task. There was also no distinction in early coarse coding for subgroups based on comprehension of implied main ideas and details. But for these same subgroups, there was a difference in sustained coarse coding. Poorer RHD comprehenders of implied information from discourse were also poorer at maintaining activation for semantically distant features of concrete nouns. Conclusions This study provides evidence of a variant of the postulated link between coarse coding and discourse comprehension in RHD. Specifically, adults with RHD who were particularly poor at sustaining activation for peripheral semantic features of nouns were also relatively poor comprehenders of implied information from narratives. PMID:20037670

Fine-coarse semantic processing in schizophrenia: a reversed pattern of hemispheric dominance.

PubMed

Zeev-Wolf, Maor; Goldstein, Abraham; Levkovitz, Yechiel; Faust, Miriam

2014-04-01

Left lateralization for language processing is a feature of neurotypical brains. In individuals with schizophrenia, lack of left lateralization is associated with the language impairments manifested in this population. Beeman׳s fine-coarse semantic coding model asserts left hemisphere specialization in fine (i.e., conventionalized) semantic coding and right hemisphere specialization in coarse (i.e., non-conventionalized) semantic coding. Applying this model to schizophrenia would suggest that language impairments in this population are a result of greater reliance on coarse semantic coding. We investigated this hypothesis and examined whether a reversed pattern of hemispheric involvement in fine-coarse semantic coding along the time course of activation could be detected in individuals with schizophrenia. Seventeen individuals with schizophrenia and 30 neurotypical participants were presented with two word expressions of four types: literal, conventional metaphoric, unrelated (exemplars of fine semantic coding) and novel metaphoric (an exemplar of coarse semantic coding). Expressions were separated by either a short (250 ms) or long (750 ms) delay. Findings indicate that whereas during novel metaphor processing, controls displayed a left hemisphere advantage at 250 ms delay and right hemisphere advantage at 750 ms, individuals with schizophrenia displayed the opposite. For conventional metaphoric and unrelated expressions, controls showed left hemisphere advantage across times, while individuals with schizophrenia showed a right hemisphere advantage. Furthermore, whereas individuals with schizophrenia were less accurate than control at judging literal, conventional metaphoric and unrelated expressions they were more accurate when judging novel metaphors. Results suggest that individuals with schizophrenia display a reversed pattern of lateralization for semantic coding which causes them to rely more heavily on coarse semantic coding. Thus, for individuals with schizophrenia, speech situation are always non-conventional, compelling them to constantly seek for meanings and prejudicing them toward novel or atypical speech acts. This, in turn, may disadvantage them in conventionalized communication and result in language impairment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.