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Sample records for latent semantic indexing

  1. Indexing by Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Deerwester, Scott; And Others

    1990-01-01

    Describes a new method for automatic indexing and retrieval called latent semantic indexing (LSI). Problems with matching query words with document words in term-based information retrieval systems are discussed, semantic structure is examined, singular value decomposition (SVD) is explained, and the mathematics underlying the SVD model is…

  2. Indexing by Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Deerwester, Scott; And Others

    1990-01-01

    Describes a new method for automatic indexing and retrieval called latent semantic indexing (LSI). Problems with matching query words with document words in term-based information retrieval systems are discussed, semantic structure is examined, singular value decomposition (SVD) is explained, and the mathematics underlying the SVD model is…

  3. Latent Semantic Indexing of medical diagnoses using UMLS semantic structures.

    PubMed Central

    Chute, C. G.; Yang, Y.; Evans, D. A.

    1991-01-01

    The relational files within the UMLS Metathesaurus contain rich semantic associations to main concepts. We invoked the technique of Latent Semantic Indexing to generate information matrices based on these relationships and created "semantic vectors" using singular value decomposition. Evaluations were made on the complete set and subsets of Metathesaurus main concepts with the semantic type "Disease or Syndrome." Real number matrices were created with main concepts, lexical variants, synonyms, and associated expressions. Ancestors, children, siblings, and related terms were added to alternative matrices, preserving the hierarchical direction of the relation as the imaginary component of a complex number. Preliminary evaluation suggests that this technique is robust. A major advantage is the exploitation of semantic features which derive from a statistical decomposition of UMLS structures, possibly reducing dependence on the tedious construction of semantic frames by humans. PMID:1807584

  4. On updating problems in latent semantic indexing

    SciTech Connect

    Simon, H.D.; Zha, H.

    1997-11-01

    The authors develop new SVD-updating algorithms for three types of updating problems arising from Latent Semantic Indexing (LSI) for information retrieval to deal with rapidly changing text document collections. They also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions than the existing algorithms and the retrieval accuracy is comparable to that obtained using the complete document collection.

  5. On updating problems in latent semantic indexing

    SciTech Connect

    Zha, H.; Simon, H.D.

    1999-10-01

    The authors develop new SVD-updating algorithms for three types of updating problems arising from latent semantic indexing (LSI) for information retrieval to deal with rapidly changing text document collections. They also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions that the existing algorithms, and the retrieval accuracy is comparable to that obtained using the complete document collection.

  6. Gene clustering by latent semantic indexing of MEDLINE abstracts.

    PubMed

    Homayouni, Ramin; Heinrich, Kevin; Wei, Lai; Berry, Michael W

    2005-01-01

    A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations. We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments. The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html.

  7. Analyzing large-scale proteomics projects with latent semantic indexing.

    PubMed

    Klie, Sebastian; Martens, Lennart; Vizcaíno, Juan Antonio; Côté, Richard; Jones, Phil; Apweiler, Rolf; Hinneburg, Alexander; Hermjakob, Henning

    2008-01-01

    Since the advent of public data repositories for proteomics data, readily accessible results from high-throughput experiments have been accumulating steadily. Several large-scale projects in particular have contributed substantially to the amount of identifications available to the community. Despite the considerable body of information amassed, very few successful analyses have been performed and published on this data, leveling off the ultimate value of these projects far below their potential. A prominent reason published proteomics data is seldom reanalyzed lies in the heterogeneous nature of the original sample collection and the subsequent data recording and processing. To illustrate that at least part of this heterogeneity can be compensated for, we here apply a latent semantic analysis to the data contributed by the Human Proteome Organization's Plasma Proteome Project (HUPO PPP). Interestingly, despite the broad spectrum of instruments and methodologies applied in the HUPO PPP, our analysis reveals several obvious patterns that can be used to formulate concrete recommendations for optimizing proteomics project planning as well as the choice of technologies used in future experiments. It is clear from these results that the analysis of large bodies of publicly available proteomics data by noise-tolerant algorithms such as the latent semantic analysis holds great promise and is currently underexploited.

  8. IPSILON: incremental parsing for semantic indexing of latent concepts.

    PubMed

    Bae, Soo Hyun; Juang, Biing-Hwang

    2010-07-01

    A new framework for content-based image retrieval, which takes advantage of the source characterization property of a universal source coding scheme, is investigated. Based upon a new class of multidimensional incremental parsing algorithm, extended from the Lempel-Ziv incremental parsing code, the proposed method captures the occurrence pattern of visual elements from a given image. A linguistic processing technique, namely the latent semantic analysis (LSA) method, is then employed to identify associative ensembles of visual elements, which lay the foundation for intelligent visual information analysis. In 2-D applications, incremental parsing decomposes an image into elementary patches that are different from the conventional fixed square-block type patches. When used in compressive representations, it is amenable in schemes that do not rely on average distortion criteria, a methodology that is a departure from the conventional vector quantization. We call this methodology a parsed representation. In this article, we present our implementations of an image retrieval system, called IPSILON, with parsed representations induced by different perceptual distortion thresholds. We evaluate the effectiveness of the use of the parsed representations by comparing their performance with that of four image retrieval systems, one using the conventional vector quantization for visual information analysis under the same LSA paradigm, another using a method called SIMPLIcity which is based upon an image segmentation and integrated region matching, and the other two based upon query-by-semantic-example and query-by-visual-example. The first two of them were tested with 20,000 images of natural scenes, and the others were tested with a portion of the images. The experimental results show that the proposed parsed representation efficiently captures the salient features in visual images and the IPSILON systems outperform other systems in terms of retrieval precision and distortion

  9. Supervised non-negative matrix factorization based latent semantic image indexing

    NASA Astrophysics Data System (ADS)

    Liang, Dong; Yang, Jie; Chang, Yuchou

    2006-05-01

    A novel latent semantic indexing (LSI) approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization (NMF) to supervised initialization is discussed. Then, supervised NMF is used in LSI to find the relationships between low-level features and high-level semantics. The retrieved results are compared with other approaches and a good performance is obtained.

  10. An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model.

    ERIC Educational Resources Information Center

    Story, Roger E.

    1996-01-01

    Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

  11. An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model.

    ERIC Educational Resources Information Center

    Story, Roger E.

    1996-01-01

    Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

  12. On matrices with low-rank-plus-shift structure: Partial SVD and latent semantic indexing

    SciTech Connect

    Zha, H.; Zhang, Z.

    1998-08-01

    The authors present a detailed analysis of matrices satisfying the so-called low-rank-plus-shift property in connection with the computation of their partial singular value decomposition. The application they have in mind is Latent Semantic Indexing for information retrieval where the term-document matrices generated from a text corpus approximately satisfy this property. The analysis is motivated by developing more efficient methods for computing and updating partial SVD of large term-document matrices and gaining deeper understanding of the behavior of the methods in the presence of noise.

  13. An evaluation of concept based latent semantic indexing for clinical information retrieval.

    PubMed Central

    Chute, C. G.; Yang, Y.

    1992-01-01

    Latent Semantic Indexing (LSI) of surgical case report text using ICD-9-CM procedure codes and index terms was evaluated. The precision-recall performance of this two-step matrix retrieval process was compared with the SMART Document retrieval system, surface word matching, and humanly assigned procedure codes. Human coding performed best, two-step LSI did less well than surface matching or SMART. This evaluation suggests that concept-based LSI may be compromised by its two-stage nature and its dependence upon a robust term database linked to main concepts. However, the potential elegance of partial- credit concept matching merits the continued evaluation of LSI for clinical case retrieval. PMID:1482949

  14. A Comparison of SVD, SVR, ADE and IRR for Latent Semantic Indexing

    NASA Astrophysics Data System (ADS)

    Zhang, Wen; Tang, Xijin; Yoshida, Taketoshi

    Recently, singular value decomposition (SVD) and its variants, which are singular value rescaling (SVR), approximation dimension equalization (ADE) and iterative residual rescaling (IRR), were proposed to conduct the job of latent semantic indexing (LSI). Although they are all based on linear algebraic method for tem-document matrix computation, which is SVD, the basic motivations behind them concerning LSI are different from each other. In this paper, a series of experiments are conducted to examine their effectiveness of LSI for the practical application of text mining, including information retrieval, text categorization and similarity measure. The experimental results demonstrate that SVD and SVR have better performances than other proposed LSI methods in the above mentioned applications. Meanwhile, ADE and IRR, because of the too much difference between their approximation matrix and original term-document matrix in Frobenius norm, can not derive good performances for text mining applications using LSI.

  15. An index-based algorithm for fast on-line query processing of latent semantic analysis

    PubMed Central

    Li, Pohan; Wang, Wei

    2017-01-01

    Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm. PMID:28520747

  16. A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing

    PubMed Central

    Wang, Guoli; Ebrahimi, Nader

    2014-01-01

    Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data. PMID:25821345

  17. A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing.

    PubMed

    Devarajan, Karthik; Wang, Guoli; Ebrahimi, Nader

    2015-04-01

    Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data.

  18. Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts.

    PubMed

    Roy, Sujoy; Curry, Brandon C; Madahian, Behrouz; Homayouni, Ramin

    2016-10-06

    The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs. For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms. LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin .

  19. Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications.

    PubMed

    Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart

    2013-01-01

    Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

  20. Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications

    PubMed Central

    Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M.; Maudsley, Stuart

    2012-01-01

    Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data. PMID:23386833

  1. Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Dumais, Susan T.

    2004-01-01

    Presents a literature review that covers the following topics related to Latent Semantic Analysis (LSA): (1) LSA overview; (2) applications of LSA, including information retrieval (IR), information filtering, cross-language retrieval, and other IR-related LSA applications; (3) modeling human memory, including the relationship of LSA to other…

  2. Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Dumais, Susan T.

    2004-01-01

    Presents a literature review that covers the following topics related to Latent Semantic Analysis (LSA): (1) LSA overview; (2) applications of LSA, including information retrieval (IR), information filtering, cross-language retrieval, and other IR-related LSA applications; (3) modeling human memory, including the relationship of LSA to other…

  3. Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts.

    PubMed

    Xu, Lijing; Furlotte, Nicholas; Lin, Yunyue; Heinrich, Kevin; Berry, Michael W; George, Ebenezer O; Homayouni, Ramin

    2011-04-14

    High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. GCAT is freely available at http://binf1.memphis.edu/gcat.

  4. Functional Cohesion of Gene Sets Determined by Latent Semantic Indexing of PubMed Abstracts

    PubMed Central

    Xu, Lijing; Furlotte, Nicholas; Lin, Yunyue; Heinrich, Kevin; Berry, Michael W.; George, Ebenezer O.; Homayouni, Ramin

    2011-01-01

    High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. Availability GCAT is freely available at http://binf1.memphis.edu/gcat PMID:21533142

  5. Latent semantic analysis.

    PubMed

    Evangelopoulos, Nicholas E

    2013-11-01

    This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

  6. Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing

    PubMed Central

    2012-01-01

    Background Identification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies. Results In this study, first we propose PSLNuc (Protein Subcellular Localization prediction for Nucleus) for predicting nuclear localization in proteins. First, for feature representation, a protein is represented by gapped-dipeptides and the feature values are weighted by homology information from a smoothed position-specific scoring matrix. After that, we incorporate probabilistic latent semantic indexing (PLSI) for feature reduction. Finally, the reduced features are used as input for a support vector machine (SVM) classifier. In addition to PSLNuc, we further identify gapped-dipeptide signatures for putative NLSs and NESs

  7. Essay Assessment with Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Miller, Tristan

    2003-01-01

    Latent semantic analysis (LSA) is an automated, statistical technique for comparing the semantic similarity of words or documents. In this article, I examine the application of LSA to automated essay scoring. I compare LSA methods to earlier statistical methods for assessing essay quality, and critically review contemporary essay-scoring systems…

  8. An Introduction to Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Landauer, Thomas K; Foltz, Peter W.; Laham, Darrell

    1998-01-01

    Offers an introduction to the theory and implementation of Latent Semantic Analysis (LSA), a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. Gives an overview of applications and modeling of human knowledge to which LSA has been applied. (SR)

  9. Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval.

    ERIC Educational Resources Information Center

    Lochbaum, Karen E.; Streeter, Lynn A.

    1989-01-01

    Describes experiments that compared a new method for automatically analyzing semantic structures in text by statistical means with the standard vector space model. Findings indicate that combining both methods improved performance over either alone. The effects of other experimental variables on retrieval performance (term weighting, suffix…

  10. From paragraph to graph: Latent semantic analysis for information visualization

    PubMed Central

    Landauer, Thomas K.; Laham, Darrell; Derr, Marcia

    2004-01-01

    Most techniques for relating textual information rely on intellectually created links such as author-chosen keywords and titles, authority indexing terms, or bibliographic citations. Similarity of the semantic content of whole documents, rather than just titles, abstracts, or overlap of keywords, offers an attractive alternative. Latent semantic analysis provides an effective dimension reduction method for the purpose that reflects synonymy and the sense of arbitrary word combinations. However, latent semantic analysis correlations with human text-to-text similarity judgments are often empirically highest at ≈300 dimensions. Thus, two- or three-dimensional visualizations are severely limited in what they can show, and the first and/or second automatically discovered principal component, or any three such for that matter, rarely capture all of the relations that might be of interest. It is our conjecture that linguistic meaning is intrinsically and irreducibly very high dimensional. Thus, some method to explore a high dimensional similarity space is needed. But the 2.7 × 107 projections and infinite rotations of, for example, a 300-dimensional pattern are impossible to examine. We suggest, however, that the use of a high dimensional dynamic viewer with an effective projection pursuit routine and user control, coupled with the exquisite abilities of the human visual system to extract information about objects and from moving patterns, can often succeed in discovering multiple revealing views that are missed by current computational algorithms. We show some examples of the use of latent semantic analysis to support such visualizations and offer views on future needs. PMID:15037748

  11. Tweets clustering using latent semantic analysis

    NASA Astrophysics Data System (ADS)

    Rasidi, Norsuhaili Mahamed; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

    2017-04-01

    Social media are becoming overloaded with information due to the increasing number of information feeds. Unlike other social media, Twitter users are allowed to broadcast a short message called as `tweet". In this study, we extract tweets related to MH370 for certain of time. In this paper, we present overview of our approach for tweets clustering to analyze the users' responses toward tragedy of MH370. The tweets were clustered based on the frequency of terms obtained from the classification process. The method we used for the text classification is Latent Semantic Analysis. As a result, there are two types of tweets that response to MH370 tragedy which is emotional and non-emotional. We show some of our initial results to demonstrate the effectiveness of our approach.

  12. Transforming Selected Concepts into Dimensions in Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Olmos, Ricardo; Jorge-Botana, Guillermo; León, José Antonio; Escudero, Inmaculada

    2014-01-01

    This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this…

  13. Transforming Selected Concepts into Dimensions in Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Olmos, Ricardo; Jorge-Botana, Guillermo; León, José Antonio; Escudero, Inmaculada

    2014-01-01

    This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this…

  14. Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding

    NASA Astrophysics Data System (ADS)

    Li, Chenxiao; Ding, Guiguang; Zhou, Jile; Guo, Yuchen; Liu, Qiang

    2014-11-01

    Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically, sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their common sparse latent semantic structure so that the learned visual dictionary is able to describe both the visual and textual properties of training data. Experiments on both image categorization and retrieval tasks demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse Coding.

  15. The Measurement of Textual Coherence with Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Foltz, Peter W.; Kintsch, Walter; Landauer, Thomas K

    1998-01-01

    Illustrates use of Latent Semantic Analysis (LSA) for predicting coherence through reanalyzing two studies that manipulated coherence of texts and assessed readers' comprehension. Finds that LSA predicts effects of text coherence on comprehension. Notes that LSA can be applied as an automated method that produces coherence predictions similar to…

  16. Effectiveness of Automated Chinese Sentence Scoring with Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Liao, Chen-Huei; Kuo, Bor-Chen; Pai, Kai-Chih

    2012-01-01

    Automated scoring by means of Latent Semantic Analysis (LSA) has been introduced lately to improve the traditional human scoring system. The purposes of the present study were to develop a LSA-based assessment system to evaluate children's Chinese sentence construction skills and to examine the effectiveness of LSA-based automated scoring function…

  17. Using Latent Semantic Analysis To Assess Knowledge: Some Technical Considerations.

    ERIC Educational Resources Information Center

    Rehder, Bob; Schreiner, M. E.; Wolfe, Michael B. W.; Laham, Darrell; Kintsch, Walter; Landauer, Thomas K

    1998-01-01

    Provides a technical analysis of the factors involved in the ability of latent semantic analysis to assess student knowledge (grading essays and matching students with appropriate instructional texts). Addresses the role of technical vocabulary, how long the student essays should be, and how one deals with the directionality of knowledge in the…

  18. A combined feature latent semantic model for scene classification

    NASA Astrophysics Data System (ADS)

    Jiang, Yue; Wang, Runsheng

    2009-10-01

    Due to vast growth of image databases, scene image classification methods have become increasingly important in computer vision areas. We propose a new scene image classification framework based on combined feature and a latent semantic model which is based on the Latent Dirichlet Allocation (LDA) in the statistical text literature. Here the model is applied to visual words representation for images. We use Gibbs sampling for parameter estimation and use several different numbers of topics at the same time to obtain the latent topic representation of images. We densely extract multi-scale patches from images and get the combined feature on these patches. Our method is unsupervised. It can also well represent semantic characteristic of images. We demonstrate the effectiveness of our approach by comparing it to those used in previous work in this area. Experiments were conducted on three often used image databases, and our method got better results than the others.

  19. Amatchmethod Based on Latent Semantic Analysis for Earthquakehazard Emergency Plan

    NASA Astrophysics Data System (ADS)

    Sun, D.; Zhao, S.; Zhang, Z.; Shi, X.

    2017-09-01

    The structure of the emergency plan on earthquake is complex, and it's difficult for decision maker to make a decision in a short time. To solve the problem, this paper presents a match method based on Latent Semantic Analysis (LSA). After the word segmentation preprocessing of emergency plan, we carry out keywords extraction according to the part-of-speech and the frequency of words. Then through LSA, we map the documents and query information to the semantic space, and calculate the correlation of documents and queries by the relation between vectors. The experiments results indicate that the LSA can improve the accuracy of emergency plan retrieval efficiently.

  20. Latent Semantic Analysis of the Languages of Life

    NASA Astrophysics Data System (ADS)

    Rossi, Ryan Anthony

    We use Latent Semantic Analysis as a basis to study the languages of life. Using this approach we derive techniques to discover latent relationships between organisms such as significant motifs and evolutionary features. Doubly Singular Value Decomposition is defined and the significance of this adaptation is demonstrated by finding a phylogeny of twenty prokaryotes. Minimal Killer Words are used to define families of organisms from negative information. The application of these words makes it possible to automatically retrieve the coding frame of a sequence from any organism.

  1. Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging.

    PubMed

    Zhang, Fan; Song, Yang; Cai, Weidong; Liu, Sidong; Liu, Siqi; Pujol, Sonia; Kikinis, Ron; Xia, Yong; Fulham, Michael J; Feng, David Dagan; Alzheimers Disease Neuroimaging Initiative

    2016-05-01

    Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.

  2. 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.

  3. Massively Parallel Latent Semantic Analyzes using a Graphics Processing Unit

    SciTech Connect

    Cavanagh, Joseph M; Cui, Xiaohui

    2009-01-01

    Latent Semantic Indexing (LSA) aims to reduce the dimensions of large Term-Document datasets using Singular Value Decomposition. However, with the ever expanding size of data sets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. The Graphics Processing Unit (GPU) can solve some highly parallel problems much faster than the traditional sequential processor (CPU). Thus, a deployable system using a GPU to speedup large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a computer cluster. Due to the GPU s application-specific architecture, harnessing the GPU s computational prowess for LSA is a great challenge. We present a parallel LSA implementation on the GPU, using NVIDIA Compute Unified Device Architecture and Compute Unified Basic Linear Algebra Subprograms. The performance of this implementation is compared to traditional LSA implementation on CPU using an optimized Basic Linear Algebra Subprograms library. After implementation, we discovered that the GPU version of the algorithm was twice as fast for large matrices (1000x1000 and above) that had dimensions not divisible by 16. For large matrices that did have dimensions divisible by 16, the GPU algorithm ran five to six times faster than the CPU version. The large variation is due to architectural benefits the GPU has for matrices divisible by 16. It should be noted that the overall speeds for the CPU version did not vary from relative normal when the matrix dimensions were divisible by 16. Further research is needed in order to produce a fully implementable version of LSA. With that in mind, the research we presented shows that the GPU is a viable option for increasing the speed of LSA, in terms of cost/performance ratio.

  4. Latent semantic analysis: a new method to measure prose recall.

    PubMed

    Dunn, John C; Almeida, Osvaldo P; Barclay, Lee; Waterreus, Anna; Flicker, Leon

    2002-02-01

    The aim of this study was to compare traditional methods of scoring the Logical Memory test of the Wechsler Memory Scale-III with a new method based on Latent Semantic Analysis (LSA). LSA represents texts as vectors in a high-dimensional semantic space and the similarity of any two texts is measured by the cosine of the angle between their respective vectors. The Logical Memory test was administered to a sample of 72 elderly individuals, 14 of whom were classified as cognitively impaired by the Mini-Mental State Examination (MMSE). The results showed that LSA was at least as valid and sensitive as traditional measures. Partial correlations between prose recall measures and measures of cognitive function indicated that LSA explained all the relationship between Logical Memory and general cognitive function. This suggests that LSA may serve as an improved measure of prose recall.

  5. Probabilistic latent semantic analysis for dynamic textures recognition and localization

    NASA Astrophysics Data System (ADS)

    Wang, Yong; Hu, Shiqiang

    2014-11-01

    We present a framework for dynamic textures (DTs) recognition and localization by using a model developed in the text analysis literature: probabilistic latent semantic analysis (pLSA). The novelty is revealed in three aspects. First, chaotic feature vector is introduced and characterizes each pixel intensity series. Next, the pLSA model is employed to discover the topics by using the bag of words representation. Finally, the spatial layout of DTs can be found. Experimental results are conducted on the well-known DTs datasets. The results show that the proposed method can successfully build DTs models and achieve higher accuracies in DTs recognition and effectively localize DTs.

  6. Randomized Probabilistic Latent Semantic Analysis for Scene Recognition

    NASA Astrophysics Data System (ADS)

    Rodner, Erik; Denzler, Joachim

    The concept of probabilistic Latent Semantic Analysis (pLSA) has gained much interest as a tool for feature transformation in image categorization and scene recognition scenarios. However, a major issue of this technique is overfitting. Therefore, we propose to use an ensemble of pLSA models which are trained using random fractions of the training data. We analyze empirically the influence of the degree of randomization and the size of the ensemble on the overall classification performance of a scene recognition task. A thoughtful evaluation shows the benefits of this approach compared to a single pLSA model.

  7. Improving knowledge management systems with latent semantic analysis

    SciTech Connect

    Sebok, A.; Plott, C.; LaVoie, N.

    2006-07-01

    Latent Semantic Analysis (LSA) offers a technique for improving lessons learned and knowledge management systems. These systems are expected to become more widely used in the nuclear industry, as experienced personnel leave and are replaced by younger, less-experienced workers. LSA is a machine learning technology that allows searching of text based on meaning rather than predefined keywords or categories. Users can enter and retrieve data using their own words, rather than relying on constrained language lists or navigating an artificially structured database. LSA-based tools can greatly enhance the usability and usefulness of knowledge management systems and thus provide a valuable tool to assist nuclear industry personnel in gathering and transferring worker expertise. (authors)

  8. Application of Latent Semantic Analysis for Open-Ended Responses in a Large, Epidemiologic Study

    DTIC Science & Technology

    2011-10-05

    2006 questionnaire cycles were included in this study (n = 108,129). To perform these analyses, Latent Semantic Analysis (LSA) was applied to a broad...of topics, most notably illness/injury, exposure, and exercise. Conclusion: These findings suggest generalized topic areas, as well as identify...the 2001-2003 and 2004-2006 questionnaire cycles were included in this study (n = 108,129). To perform these analyses, Latent Semantic Analysis

  9. Parallel Latent Semantic Analysis using a Graphics Processing Unit

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E; Cavanagh, Joseph M

    2009-01-01

    Latent Semantic Analysis (LSA) can be used to reduce the dimensions of large Term-Document datasets using Singular Value Decomposition. However, with the ever expanding size of data sets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. The Graphics Processing Unit (GPU) can solve some highly parallel problems much faster than the traditional sequential processor (CPU). Thus, a deployable system using a GPU to speedup large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a computer cluster. In this paper, we presented a parallel LSA implementation on the GPU, using NVIDIA Compute Unified Device Architecture (CUDA) and Compute Unified Basic Linear Algebra Subprograms (CUBLAS). The performance of this implementation is compared to traditional LSA implementation on CPU using an optimized Basic Linear Algebra Subprograms library. For large matrices that have dimensions divisible by 16, the GPU algorithm ran five to six times faster than the CPU version.

  10. Enhancing multilingual latent semantic analysis with term alignment information.

    SciTech Connect

    Chew, Peter A.; Bader, Brett William

    2008-08-01

    Latent Semantic Analysis (LSA) is based on the Singular Value Decomposition (SVD) of a term-by-document matrix for identifying relationships among terms and documents from co-occurrence patterns. Among the multiple ways of computing the SVD of a rectangular matrix X, one approach is to compute the eigenvalue decomposition (EVD) of a square 2 x 2 composite matrix consisting of four blocks with X and XT in the off-diagonal blocks and zero matrices in the diagonal blocks. We point out that significant value can be added to LSA by filling in some of the values in the diagonal blocks (corresponding to explicit term-to-term or document-to-document associations) and computing a term-by-concept matrix from the EVD. For the case of multilingual LSA, we incorporate information on cross-language term alignments of the same sort used in Statistical Machine Translation (SMT). Since all elements of the proposed EVD-based approach can rely entirely on lexical statistics, hardly any price is paid for the improved empirical results. In particular, the approach, like LSA or SMT, can still be generalized to virtually any language(s); computation of the EVD takes similar resources to that of the SVD since all the blocks are sparse; and the results of EVD are just as economical as those of SVD.

  11. Measuring Discourse-Level Processes with Verbal Protocols and Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Millis, Keith; Magliano, Joseph; Todaro, Stacey

    2006-01-01

    The present study used latent semantic analysis (LSA) to analyze verbal protocols that were collected while participants read expository passages. In the study, participants were asked to type their thoughts after reading each sentence of 2 scientific texts. The semantic similarity between the protocols and the current sentence and prior causal…

  12. Assessing Reading Skill with a Think-Aloud Procedure and Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Magliano, Joseph P.; Millis, Keith K.

    2003-01-01

    Two studies examined the viability of assessing reading strategies using a think-aloud protocol combined with latent semantic analysis (LSA). Findings demonstrated that the responses of less-skilled readers semantically overlapped more with focal sentences than with causal antecedent sentences, whereas skilled readers' responses overlapped with…

  13. MASSIVELY PARALLEL LATENT SEMANTIC ANALYSES USING A GRAPHICS PROCESSING UNIT

    SciTech Connect

    Cavanagh, J.; Cui, S.

    2009-01-01

    Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using Singular Value Decomposition. However, with the ever-expanding size of datasets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. A graphics processing unit (GPU) can solve some highly parallel problems much faster than a traditional sequential processor or central processing unit (CPU). Thus, a deployable system using a GPU to speed up large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a PC cluster. Due to the GPU’s application-specifi c architecture, harnessing the GPU’s computational prowess for LSA is a great challenge. We presented a parallel LSA implementation on the GPU, using NVIDIA® Compute Unifi ed Device Architecture and Compute Unifi ed Basic Linear Algebra Subprograms software. The performance of this implementation is compared to traditional LSA implementation on a CPU using an optimized Basic Linear Algebra Subprograms library. After implementation, we discovered that the GPU version of the algorithm was twice as fast for large matrices (1 000x1 000 and above) that had dimensions not divisible by 16. For large matrices that did have dimensions divisible by 16, the GPU algorithm ran fi ve to six times faster than the CPU version. The large variation is due to architectural benefi ts of the GPU for matrices divisible by 16. It should be noted that the overall speeds for the CPU version did not vary from relative normal when the matrix dimensions were divisible by 16. Further research is needed in order to produce a fully implementable version of LSA. With that in mind, the research we presented shows that the GPU is a viable option for increasing the speed of LSA, in terms of cost/performance ratio.

  14. Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

    ERIC Educational Resources Information Center

    Makovoz, Gennadiy

    2010-01-01

    The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

  15. Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

    ERIC Educational Resources Information Center

    Makovoz, Gennadiy

    2010-01-01

    The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

  16. Learning from Text: Matching Readers and Texts by Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Wolfe, Michael B. W.; Schreiner, M. E.; Rehder, Bob; Laham, Darrell; Foltz, Peter W.; Kintsch, Walter; Landauer, Thomas K

    1998-01-01

    Uses Latent Semantic Analysis (LSA) to predict how much readers would learn from texts based on the estimated conceptual match between their topic knowledge and the text information. Shows a nonmonotonic relationship in which learning was greatest for texts that were neither too easy nor too difficult. Finds LSA was as effective at predicting…

  17. Latent Semantic Analysis: A Theory of the Psychology of Language and Mind.

    ERIC Educational Resources Information Center

    Landauer, Thomas K.

    1999-01-01

    Contributes to communication theory and research by adding to a discussion of a computational model called latent semantic analysis (LSA). Argues that LSA does not handle all aspects of language processing, but offers a biologically and psychologically plausible mechanistic explanation of the acquisition, induction, and representation of verbal…

  18. Semantic-Aware Co-Indexing for Image Retrieval.

    PubMed

    Zhang, Shiliang; Yang, Ming; Wang, Xiaoyu; Lin, Yuanqing; Tian, Qi

    2015-12-01

    In content-based image retrieval, inverted indexes allow fast access to database images and summarize all knowledge about the database. Indexing multiple clues of image contents allows retrieval algorithms search for relevant images from different perspectives, which is appealing to deliver satisfactory user experiences. However, when incorporating diverse image features during online retrieval, it is challenging to ensure retrieval efficiency and scalability. In this paper, for large-scale image retrieval, we propose a semantic-aware co-indexing algorithm to jointly embed two strong cues into the inverted indexes: 1) local invariant features that are robust to delineate low-level image contents, and 2) semantic attributes from large-scale object recognition that may reveal image semantic meanings. Specifically, for an initial set of inverted indexes of local features, we utilize semantic attributes to filter out isolated images and insert semantically similar images to this initial set. Encoding these two distinct and complementary cues together effectively enhances the discriminative capability of inverted indexes. Such co-indexing operations are totally off-line and introduce small computation overhead to online retrieval, because only local features but no semantic attributes are employed for the query. Hence, this co-indexing is different from existing image retrieval methods fusing multiple features or retrieval results. Extensive experiments and comparisons with recent retrieval methods manifest the competitive performance of our method.

  19. More data trumps smarter algorithms: comparing pointwise mutual information with latent semantic analysis.

    PubMed

    Recchia, Gabriel; Jones, Michael N

    2009-08-01

    Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for experimental stimulus selection and for evaluating a model's cognitive plausibility as a mechanism that people might use to organize meaning in memory. Although humans are exposed to enormous quantities of speech, practical constraints limit the amount of data that many current computational models can learn from. We follow up on previous work evaluating a simple metric of pointwise mutual information. Controlling for confounds in previous work, we demonstrate that this metric benefits from training on extremely large amounts of data and correlates more closely with human semantic similarity ratings than do publicly available implementations of several more complex models. We also present a simple tool for building simple and scalable models from large corpora quickly and efficiently.

  20. Latent morpho-semantic analysis : multilingual information retrieval with character n-grams and mutual information.

    SciTech Connect

    Bader, Brett William; Chew, Peter A.; Abdelali, Ahmed

    2008-08-01

    We describe an entirely statistics-based, unsupervised, and language-independent approach to multilingual information retrieval, which we call Latent Morpho-Semantic Analysis (LMSA). LMSA overcomes some of the shortcomings of related previous approaches such as Latent Semantic Analysis (LSA). LMSA has an important theoretical advantage over LSA: it combines well-known techniques in a novel way to break the terms of LSA down into units which correspond more closely to morphemes. Thus, it has a particular appeal for use with morphologically complex languages such as Arabic. We show through empirical results that the theoretical advantages of LMSA can translate into significant gains in precision in multilingual information retrieval tests. These gains are not matched either when a standard stemmer is used with LSA, or when terms are indiscriminately broken down into n-grams.

  1. Adapting Spectral Co-clustering to Documents and Terms Using Latent Semantic Analysis

    NASA Astrophysics Data System (ADS)

    Park, Laurence A. F.; Leckie, Christopher A.; Ramamohanarao, Kotagiri; Bezdek, James C.

    Spectral co-clustering is a generic method of computing co-clusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. We show that both spectral co-clustering and LSA follow the same process, using different normalisation schemes and metrics. By combining the properties of the two co-clustering methods, we obtain an improved co-clustering method for document-term relational data that provides an increase in the cluster quality of 33.0%.

  2. Latent Semantics of Action Verbs Reflect Phonetic Parameters of Intensity and Emotional Content

    PubMed Central

    Petersen, Michael Kai

    2015-01-01

    Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 × 20 emotion-, face-, and hand-related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora: HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, and projecting them into an articulatory space framed by tongue height and formant frequencies, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes. PMID:25849977

  3. Latent semantics of action verbs reflect phonetic parameters of intensity and emotional content.

    PubMed

    Petersen, Michael Kai

    2015-01-01

    Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 × 20 emotion-, face-, and hand-related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora: HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, and projecting them into an articulatory space framed by tongue height and formant frequencies, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes.

  4. Simulating Expert Clinical Comprehension: Adapting Latent Semantic Analysis to Accurately Extract Clinical Concepts from Psychiatric Narrative

    PubMed Central

    Cohen, Trevor; Blatter, Brett; Patel, Vimla

    2008-01-01

    Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested. PMID:18455483

  5. Semantic overlay network for large-scale spatial information indexing

    NASA Astrophysics Data System (ADS)

    Zou, Zhiqiang; Wang, Yue; Cao, Kai; Qu, Tianshan; Wang, Zhongmin

    2013-08-01

    The increased demand for online services of spatial information poses new challenges to the combined filed of Computer Science and Geographic Information Science. Amongst others, these include fast indexing of spatial data in distributed networks. In this paper we propose a novel semantic overlay network for large-scale multi-dimensional spatial information indexing, called SON_LSII, which has a hybrid structure integrating a semantic quad-tree and Chord ring. The SON_LSII is a small world overlay network that achieves a very competitive trade-off between indexing efficiency and maintenance overhead. To create SON_LSII, we use an effective semantic clustering strategy that considers two aspects, i.e., the semantic of spatial information that peer holds in overlay network and physical network performances. Based on SON_LSII, a mapping method is used to reduce the multi-dimensional features into a single dimension and an efficient indexing algorithm is presented to support complex range queries of the spatial information with a massive number of concurrent users. The results from extensive experiments demonstrate that SON_LSII is superior to existing overlay networks in various respects, including scalability, maintenance, rate of indexing hits, indexing logical hops, and adaptability. Thus, the proposed SON_LSII can be used for large-scale spatial information indexing.

  6. A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique

    NASA Astrophysics Data System (ADS)

    Shirkhodaie, Amir; Elangovan, Vinayak; Alkilani, Amjad; Habibi, Mohammad

    2013-05-01

    This paper presents an ongoing effort towards development of an intelligent Decision-Support System (iDSS) for fusion of information from multiple sources consisting of data from hard (physical sensors) and soft (textural sources. Primarily, this paper defines taxonomy of decision support systems for latent semantic data mining from heterogeneous data sources. A Probabilistic Latent Semantic Analysis (PLSA) approach is proposed for latent semantic concepts search from heterogeneous data sources. An architectural model for generating semantic annotation of multi-modality sensors in a modified Transducer Markup Language (TML) is described. A method for TML messages fusion is discussed for alignment and integration of spatiotemporally correlated and associated physical sensory observations. Lastly, the experimental results which exploit fusion of soft/hard sensor sources with support of iDSS are discussed.

  7. Computerizing reading training: evaluation of a latent semantic analysis space for science text.

    PubMed

    Kurby, Christopher A; Wiemer-Hastings, Katja; Ganduri, Nagasai; Magliano, Joseph P; Millis, Keith K; McNamara, Danielle S

    2003-05-01

    The effectiveness of a domain-specific latent semantic analysis (LSA) in assessing reading strategies was examined. Students were given self-explanation reading training (SERT) and asked to think aloud after each sentence in a science text. Novice and expert human raters and two LSA spaces (general reading, science) rated the similarity of each think-aloud protocol to benchmarks representing three different reading strategies (minimal, local, and global). The science LSA space correlated highly with human judgments, and more highly than did the general reading space. Also, cosines from the science LSA spaces can distinguish between different levels of semantic similarity, but may have trouble in distinguishing local processing protocols. Thus, a domain-specific LSA space is advantageous regardless of the size of the space. The results are discussed in the context of applying the science LSA to a computer-based version of SERT that gives online feedback based on LSA cosines.

  8. Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis

    DTIC Science & Technology

    2004-01-01

    SVD). In this paper, we generally call such SVD- based subspace construction spectral analysis. Latent Semantic Indexing (LSI) (Deerwester et al...Richard Harshman. 1990. Indexing by Latent Semantic Analysis. Journal of the Society for Information Science, 41:391–407. A. Dempster, N. Laird, and D...and Knowledge Management. Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh Vempala. 2000. Latent Semantic Indexing: A

  9. MEDTAG: tag-like semantics for medical document indexing.

    PubMed

    Ruch, P; Wagner, J; Bouillon, P; Baud, R H; Rassinoux, A M; Scherrer, J R

    1999-01-01

    Medical documentation is central in health care, as it constitutes the main means of communication between care providers. However, there is a gap to bridge between storing information and extracting the relevant underlying knowledge. We believe natural language processing (NLP) is the best solution to handle such a large amount of textual information. In this paper we describe the construction of a semantic tagset for medical document indexing purposes. Rather than attempting to produce a home-made tagset, we decided to use, as far as possible, standard medicine resources. This step has led us to choose UMLS hierarchical classes as a basis for our tagset. We also show that semantic tagging is not only providing bases for disambiguisation between senses, but is also useful in the query expansion process of the retrieval system. We finally focus on assessing the results of the semantic tagger.

  10. Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

    PubMed Central

    Gießler, Paul; Ohnesorge-Radtke, Ursula; Spreckelsen, Cord

    2015-01-01

    Background The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM’s semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms. Objective The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM. Methods In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged. Results The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users. Conclusions It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively. PMID:27731860

  11. Validating Quantitative Measurement Using Qualitative Data: Combining Rasch Scaling and Latent Semantic Analysis in Psychiatry

    NASA Astrophysics Data System (ADS)

    Lange, Rense

    2015-02-01

    An extension of concurrent validity is proposed that uses qualitative data for the purpose of validating quantitative measures. The approach relies on Latent Semantic Analysis (LSA) which places verbal (written) statements in a high dimensional semantic space. Using data from a medical / psychiatric domain as a case study - Near Death Experiences, or NDE - we established concurrent validity by connecting NDErs qualitative (written) experiential accounts with their locations on a Rasch scalable measure of NDE intensity. Concurrent validity received strong empirical support since the variance in the Rasch measures could be predicted reliably from the coordinates of their accounts in the LSA derived semantic space (R2 = 0.33). These coordinates also predicted NDErs age with considerable precision (R2 = 0.25). Both estimates are probably artificially low due to the small available data samples (n = 588). It appears that Rasch scalability of NDE intensity is a prerequisite for these findings, as each intensity level is associated (at least probabilistically) with a well- defined pattern of item endorsements.

  12. Predicting Raters’ Transparency Judgments of English and Chinese Morphological Constituents using Latent Semantic Analysis

    PubMed Central

    Wang, Hsueh-Cheng; Hsu, Li-Chuan; Tien, Yi-Min; Pomplun, Marc

    2013-01-01

    The morphological constituents of English compounds (e.g., “butter” and “fly” for “butterfly”) and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make the judgments difficult, and a computational alternative based on a general model may be a way to average across subjective differences. The current study proposes two approaches based on Latent Semantic Analysis (Landauer & Dumais, 1997): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent based on a clustering analysis of morphological family members (e.g., “butterfingers” or “buttermilk” for “butter”). The proposed models successfully predicted participants’ transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, due to raters’ morphological processing in different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed. PMID:23784009

  13. Using latent semantic analysis and the predication algorithm to improve extraction of meanings from a diagnostic corpus.

    PubMed

    Jorge-Botana, Guillermo; Olmos, Ricardo; León, José Antonio

    2009-11-01

    There is currently a widespread interest in indexing and extracting taxonomic information from large text collections. An example is the automatic categorization of informally written medical or psychological diagnoses, followed by the extraction of epidemiological information or even terms and structures needed to formulate guiding questions as an heuristic tool for helping doctors. Vector space models have been successfully used to this end (Lee, Cimino, Zhu, Sable, Shanker, Ely & Yu, 2006; Pakhomov, Buntrock & Chute, 2006). In this study we use a computational model known as Latent Semantic Analysis (LSA) on a diagnostic corpus with the aim of retrieving definitions (in the form of lists of semantic neighbors) of common structures it contains (e.g. "storm phobia", "dog phobia") or less common structures that might be formed by logical combinations of categories and diagnostic symptoms (e.g. "gun personality" or "germ personality"). In the quest to bring definitions into line with the meaning of structures and make them in some way representative, various problems commonly arise while recovering content using vector space models. We propose some approaches which bypass these problems, such as Kintsch's (2001) predication algorithm and some corrections to the way lists of neighbors are obtained, which have already been tested on semantic spaces in a non-specific domain (Jorge-Botana, León, Olmos & Hassan-Montero, under review). The results support the idea that the predication algorithm may also be useful for extracting more precise meanings of certain structures from scientific corpora, and that the introduction of some corrections based on vector length may increases its efficiency on non-representative terms.

  14. The Use of Latent Semantic Analysis as a Tool for the Quantitative Assessment of Understanding and Knowledge.

    ERIC Educational Resources Information Center

    Shapiro, Amy M.; McNamara, Danielle S.

    2000-01-01

    Discusses latent semantic analysis (LSA), a statistical model for representing word usage in written language, and describes two experiments that were conducted with undergraduates to determine what aspect of knowledge, conceptual or factual, is being reflected in an LSA output from student essays. (Contains 21 references.)u (Author/LRW)

  15. A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

    ERIC Educational Resources Information Center

    Landauer, Thomas K; Dumais, Susan T.

    1997-01-01

    A theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented to explain how people know as much as they do with the little information they get. LSA suggests that some domains of knowledge contain vast numbers of weak intercorrelations that amplify learning by inference. (SLD)

  16. Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis

    NASA Astrophysics Data System (ADS)

    Müller, Oliver; Schmiedel, Theresa; Gorbacheva, Elena; vom Brocke, Jan

    2016-01-01

    While researchers have analysed the organisational competences that are required for successful Business Process Management (BPM) initiatives, individual BPM competences have not yet been studied in detail. In this study, latent semantic analysis is used to examine a collection of 1507 BPM-related job advertisements in order to develop a typology of BPM professionals. This empirical analysis reveals distinct ideal types and profiles of BPM professionals on several levels of abstraction. A closer look at these ideal types and profiles confirms that BPM is a boundary-spanning field that requires interdisciplinary sets of competence that range from technical competences to business and systems competences. Based on the study's findings, it is posited that individual and organisational alignment with the identified ideal types and profiles is likely to result in high employability and organisational BPM success.

  17. Applying latent semantic analysis to large-scale medical image databases.

    PubMed

    Stathopoulos, Spyridon; Kalamboukis, Theodore

    2015-01-01

    Latent Semantic Analysis (LSA) although has been used successfully in text retrieval when applied to CBIR induces scalability issues with large image collections. The method so far has been used with small collections due to the high cost of storage and computational time for solving the SVD problem for a large and dense feature matrix. Here we present an effective and efficient approach of applying LSA skipping the SVD solution of the feature matrix and overcoming in this way the deficiencies of the method with large scale datasets. Early and late fusion techniques are tested and their performance is calculated. The study demonstrates that early fusion of several composite descriptors with visual words increase retrieval effectiveness. It also combines well in a late fusion for mixed (textual and visual) ad hoc and modality classification. The results reported are comparable to state of the art algorithms without including additional knowledge from the medical domain.

  18. Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis

    NASA Astrophysics Data System (ADS)

    Yi, Wenbin; Tang, Hong

    2009-10-01

    In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block image will get better classification results.

  19. Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies.

    PubMed

    Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

    2016-01-01

    In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are represented as vectors in a high-dimensional vector space. This allows for computing word similarities as the cosine of the angle between two such vectors. In two experiments, we investigated whether LSA cosine similarities predict priming effects, in that higher cosine similarities are associated with shorter reaction times (RTs). Critically, we applied a pseudo-random procedure in generating the item material to ensure that we directly manipulated LSA cosines as an independent variable. We employed two lexical priming experiments with lexical decision tasks (LDTs). In Experiment 1 we presented participants with 200 different prime words, each paired with one unique target. We found a significant effect of cosine similarities on RTs. The same was true for Experiment 2, where we reversed the prime-target order (primes of Experiment 1 were targets in Experiment 2, and vice versa). The results of these experiments confirm that LSA cosine similarities can predict priming effects, supporting the view that they are psychologically relevant. The present study thereby provides evidence for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure. However, it is also shown that other DSMs can outperform LSA as a predictor of priming effects.

  20. Latent semantic variables are associated with formal thought disorder and adaptive behavior in older inpatients with schizophrenia

    PubMed Central

    Holshausen, Katherine; Harvey, Philip D.; Elvevåg, Brita; Foltz, Peter W.; Bowie, Christopher R.

    2013-01-01

    Introduction Formal thought disorder is a hallmark feature of schizophrenia in which disorganized thoughts manifest as disordered speech. A dysfunctional semantic system and a disruption in executive functioning have been proposed as possible mechanisms for formal thought disorder and verbal fluency impairment. Traditional rating scales and neuropsychological test scores might not be sensitive enough to distinguish among types of semantic impairments. This has lead to the proposed used of a natural language processing technique, Latent Semantic Analysis (LSA), which offers improved semantic sensitivity. Method In this study, LSA, a computational, vector-based text analysis technique to examine the contribution of vector length, an LSA measure related to word unusualness and cosines between word vectors, an LSA measure of semantic coherence to semantic and phonological fluency, disconnectedness of speech, and adaptive functioning in 165 older inpatients with schizophrenia. Results In stepwise regressions word unusualness was significantly associated with semantic fluency and phonological fluency, disconnectedness in speech, and impaired functioning, even after considering the contribution of pre-morbid cognition, positive and negative symptoms, and demographic variables. Conclusions These findings support the utility of LSA in examining the contribution of coherence to thought disorder and the its relationship with daily functioning. Deficits in verbal fluency may be an expression of underlying disorganization in thought processes. PMID:23510635

  1. Transmembrane helix prediction using amino acid property features and latent semantic analysis.

    PubMed

    Ganapathiraju, Madhavi; Balakrishnan, N; Reddy, Raj; Klein-Seetharaman, Judith

    2008-01-01

    Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology "cytoplasmic-transmembrane-extracellular" and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families. Here, we describe a new method "TMpro" to predict TM helices with high accuracy. To avoid overfitting to existing topologies, we have collapsed cytoplasmic and extracellular labels to a single state, non-TM. TMpro is a binary classifier which predicts TM or non-TM using multiple amino acid properties (charge, polarity, aromaticity, size and electronic properties) as features. The features are extracted from sequence information by applying the framework used for latent semantic analysis of text documents and are input to neural networks that learn the distinction between TM and non-TM segments. The model uses only 25 free parameters. In benchmark analysis TMpro achieves 95% segment F-score corresponding to 50% reduction in error rate compared to the best methods not requiring an evolutionary profile of a protein to be known. Performance is also improved when applied to more recent and larger high resolution datasets PDBTM and MPtopo. TMpro predictions in membrane proteins with unusual or disputed TM structure (K+ channel, aquaporin and HIV envelope glycoprotein) are discussed. TMpro uses very few free parameters in modeling TM segments as opposed to the very large number of free parameters used in state-of-the-art membrane prediction methods, yet achieves very

  2. Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach.

    PubMed

    Nicodemus, Kristin K; Elvevåg, Brita; Foltz, Peter W; Rosenstein, Mark; Diaz-Asper, Catherine; Weinberger, Daniel R

    2014-06-01

    Category fluency is a widely used task that relies on multiple neurocognitive processes and is a sensitive assay of cortical dysfunction, including in schizophrenia. The test requires naming of as many words belonging to a certain category (e.g., animals) as possible within a short period of time. The core metrics are the overall number of words produced and the number of errors, namely non-members generated for a target category. We combine a computational linguistic approach with a candidate gene approach to examine the genetic architecture of this traditional fluency measure. In addition to the standard metric of overall word count, we applied a computational approach to semantics, Latent Semantic Analysis (LSA), to analyse the clustering pattern of the categories generated, as it likely reflects the search in memory for meanings. Also, since fluency performance probably also recruits verbal learning and recall processes, we included two standard measures of this cognitive process: the Wechsler Memory Scale and California Verbal Learning Test (CVLT). To explore the genetic architecture of traditional and LSA-derived fluency measures we employed a candidate gene approach focused on SNPs with known function that were available from a recent genome-wide association study (GWAS) of schizophrenia. The selected candidate genes were associated with language and speech, verbal learning and recall processes, and processing speed. A total of 39 coding SNPs were included for analysis in 665 subjects. Given the modest sample size, the results should be regarded as exploratory and preliminary. Nevertheless, the data clearly illustrate how extracting the meaning from participants' responses, by analysing the actual content of words, generates useful and neurocognitively viable metrics. We discuss three replicated SNPs in the genes ZNF804A, DISC1 and KIAA0319, as well as the potential for computational analyses of linguistic and textual data in other genomics tasks. Copyright

  3. Comparison of Human and Latent Semantic Analysis (LSA) Judgements of Pairwise Document Similarities for a News Corpus

    DTIC Science & Technology

    2004-09-01

    documents. Previously, assessments of the similarity of the judgements of these techniques to those of people have been carried out on data sets where...Various local and global weighting schemes are available for LSA. These alter the level of correlation with human judgements as does the choice of the...number of singular values to use in the construction of the latent semantic space. The effects of the settings of these variables on the correlation of

  4. Exploring dangerous neighborhoods: Latent Semantic Analysis and computing beyond the bounds of the familiar

    PubMed Central

    Cohen, Trevor; Blatter, Brett; Patel, Vimla

    2005-01-01

    Certain applications require computer systems to approximate intended human meaning. This is achievable in constrained domains with a finite number of concepts. Areas such as psychiatry, however, draw on concepts from the world-at-large. A knowledge structure with broad scope is required to comprehend such domains. Latent Semantic Analysis (LSA) is an unsupervised corpus-based statistical method that derives quantitative estimates of the similarity between words and documents from their contextual usage statistics. The aim of this research was to evaluate the ability of LSA to derive meaningful associations between concepts relevant to the assessment of dangerousness in psychiatry. An expert reference model of dangerousness was used to guide the construction of a relevant corpus. Derived associations between words in the corpus were evaluated qualitatively. A similarity-based scoring function was used to assign dangerousness categories to discharge summaries. LSA was shown to derive intuitive relationships between concepts and correlated significantly better than random with human categorization of psychiatric discharge summaries according to dangerousness. The use of LSA to derive a simulated knowledge structure can extend the scope of computer systems beyond the boundaries of constrained conceptual domains. PMID:16779020

  5. Annotations of Mexican bullfighting videos for semantic index

    NASA Astrophysics Data System (ADS)

    Montoya Obeso, Abraham; Oropesa Morales, Lester Arturo; Fernando Vázquez, Luis; Cocolán Almeda, Sara Ivonne; Stoian, Andrei; García Vázquez, Mireya Saraí; Zamudio Fuentes, Luis Miguel; Montiel Perez, Jesús Yalja; de la O Torres, Saul; Ramírez Acosta, Alejandro Alvaro

    2015-09-01

    The video annotation is important for web indexing and browsing systems. Indeed, in order to evaluate the performance of video query and mining techniques, databases with concept annotations are required. Therefore, it is necessary generate a database with a semantic indexing that represents the digital content of the Mexican bullfighting atmosphere. This paper proposes a scheme to make complex annotations in a video in the frame of multimedia search engine project. Each video is partitioned using our segmentation algorithm that creates shots of different length and different number of frames. In order to make complex annotations about the video, we use ELAN software. The annotations are done in two steps: First, we take note about the whole content in each shot. Second, we describe the actions as parameters of the camera like direction, position and deepness. As a consequence, we obtain a more complete descriptor of every action. In both cases we use the concepts of the TRECVid 2014 dataset. We also propose new concepts. This methodology allows to generate a database with the necessary information to create descriptors and algorithms capable to detect actions to automatically index and classify new bullfighting multimedia content.

  6. 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.

  7. Discovering discovery patterns with predication-based Semantic Indexing

    PubMed Central

    Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W.; Davies, Peter; Rindflesch, Thomas C.

    2012-01-01

    In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as “discovery patterns”, such as “drug x INHIBITS substance y, substance y CAUSES disease z” that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues. PMID:22841748

  8. Discovering discovery patterns with Predication-based Semantic Indexing.

    PubMed

    Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W; Davies, Peter; Rindflesch, Thomas C

    2012-12-01

    In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as "discovery patterns", such as "drug x INHIBITS substance y, substance y CAUSES disease z" that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

    PubMed

    Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

    2014-11-01

    Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Coherent Semantic-Visual Indexing for Large-Scale Image Retrieval in the Cloud.

    PubMed

    Hong, Richang; Li, Lei; Cai, Junjie; Tao, Dapeng; Wang, Meng; Tian, Qi

    2017-09-01

    The rapidly increasing number of images on the internet has further increased the need for efficient indexing for digital image searching of large databases. The design of a cloud service that provides high efficiency but compact image indexing remains challenging, partly due to the well-known semantic gap between user queries and the rich semantics of large-scale data sets. In this paper, we construct a novel joint semantic-visual space by leveraging visual descriptors and semantic attributes, which narrows the semantic gap by combining both attributes and indexing into a single framework. Such a joint space embraces the flexibility of coherent semantic-visual indexing, which employs binary codes to boost retrieval speed while maintaining accuracy. To solve the proposed model, we make the following contributions. First, we propose an interactive optimization method to find the joint semantic and visual descriptor space. Second, we prove convergence of our optimization algorithm, which guarantees a good solution after a certain number of iterations. Third, we integrate the semantic-visual joint space system with spectral hashing, which finds an efficient solution to search up to billion-scale data sets. Finally, we design an online cloud service to provide a more efficient online multimedia service. Experiments on two standard retrieval datasets (i.e., Holidays1M, Oxford5K) show that the proposed method is promising compared with the current state-of-the-art and that the cloud system significantly improves performance.

  11. N400-like Potentials and Reaction Times Index Semantic Relations between Highly Repeated Individual Words

    ERIC Educational Resources Information Center

    Renoult, Louis; Debruille, J. Bruno

    2011-01-01

    The N400 ERP is an electrophysiological index of semantic processing. Its amplitude varies with the semantic category of words, their concreteness, or whether their meaning matches that of a preceding context. The results of a number of studies suggest that these effects could be markedly reduced or suppressed for stimuli that are repeated.…

  12. Context as the Building Blocks of Meaning: A Retrieval Model for the Semantic Representation of Words

    DTIC Science & Technology

    2003-04-01

    the question in a computational model called Latent Semantic Analysis (LSA). A word’s meaning in LSA is a vector describing the frequency with which...References 1. Landauer, T. K. & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of the acquisition...Dumais, S.T. (1994). Latent Semantic Indexing (LSI) and TREC-2. In D. Harnan (Ed.) The second text retrieval conference (TREC2) (National Institute of

  13. The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text.

    PubMed

    Altszyler, Edgar; Ribeiro, Sidarta; Sigman, Mariano; Fernández Slezak, Diego

    2017-09-21

    Computer-based dreams content analysis relies on word frequencies within predefined categories in order to identify different elements in text. As a complementary approach, we explored the capabilities and limitations of word-embedding techniques to identify word usage patterns among dream reports. These tools allow us to quantify words associations in text and to identify the meaning of target words. Word-embeddings have been extensively studied in large datasets, but only a few studies analyze semantic representations in small corpora. To fill this gap, we compared Skip-gram and Latent Semantic Analysis (LSA) capabilities to extract semantic associations from dream reports. LSA showed better performance than Skip-gram in small size corpora in two tests. Furthermore, LSA captured relevant word associations in dream collection, even in cases with low-frequency words or small numbers of dreams. Word associations in dreams reports can thus be quantified by LSA, which opens new avenues for dream interpretation and decoding. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

    ERIC Educational Resources Information Center

    Leite, Walter L.; Stapleton, Laura M.

    2011-01-01

    In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

  15. Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

    ERIC Educational Resources Information Center

    Leite, Walter L.; Stapleton, Laura M.

    2011-01-01

    In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

  16. Predication-based semantic indexing: permutations as a means to encode predications in semantic space.

    PubMed

    Cohen, Trevor; Schvaneveldt, Roger W; Rindflesch, Thomas C

    2009-11-14

    Corpus-derived distributional models of semantic distance between terms have proved useful in a number of applications. For both theoretical and practical reasons, it is desirable to extend these models to encode discrete concepts and the ways in which they are related to one another. In this paper, we present a novel vector space model that encodes semantic predications derived from MEDLINE by the SemRep system into a compact spatial representation. The associations captured by this method are of a different and complementary nature to those derived by traditional vector space models, and the encoding of predication types presents new possibilities for knowledge discovery and information retrieval.

  17. 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.

  18. Visualization of semantic indexing similarity over MeSH.

    PubMed

    Du, Haixia; Yoo, Terry S

    2007-10-11

    We present an interactive visualization system for the evaluation of indexing results of the MEDLINE data-base over the Medical Subject Headings (MeSH) structure in a graphical radial-tree layout. It displays indexing similarity measurements with 2D color coding and a 3D height field permitting the evaluation of the automatic Medical Text Indexer (MTI), compared with human indexers.

  19. Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features.

    PubMed

    Shen, Yao; Guturu, Parthasarathy Partha; Buckles, Bill P

    2012-01-01

    Since wireless capsule endoscopy (WCE) is a novel technology for recording the videos of the digestive tract of a patient, the problem of segmenting the WCE video of the digestive tract into subvideos corresponding to the entrance, stomach, small intestine, and large intestine regions is not well addressed in the literature. A selected few papers addressing this problem follow supervised leaning approaches that presume availability of a large database of correctly labeled training samples. Considering the difficulties in procuring sizable WCE training data sets needed for achieving high classification accuracy, we introduce in this paper an unsupervised learning approach that employs Scale Invariant Feature Transform (SIFT) for extraction of local image features and the probabilistic latent semantic analysis (pLSA) model used in the linguistic content analysis for data clustering. Results of experimentation indicate that this method compares well in classification accuracy with the state-of-the-art supervised classification approaches to WCE video segmentation.

  20. Semantic Indexing of Terrasar-X and in Situ Data for Urban Analytics

    NASA Astrophysics Data System (ADS)

    Espinoza Molina, D.; Alonso, K.; Datcu, M.

    2015-12-01

    This paper presents the semantic indexing of TerraSAR-X images and in situ data. Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land cover catalogue. All the generated information is stored into a geo-database supporting the link between different types of information and the computation of queries and analytics. We used 11 TerraSAR-X scenes over Germany and LUCAS as in situ data. The semantic index is composed of about 73 land use land cover categories found in TerraSAR-X test dataset and 84 categories found in LUCAS dataset.

  1. The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models.

    PubMed

    McNeish, Daniel; An, Ji; Hancock, Gregory R

    2017-03-02

    Latent variable modeling is a popular and flexible statistical framework. Concomitant with fitting latent variable models is assessment of how well the theoretical model fits the observed data. Although firm cutoffs for these fit indexes are often cited, recent statistical proofs and simulations have shown that these fit indexes are highly susceptible to measurement quality. For instance, a root mean square error of approximation (RMSEA) value of 0.06 (conventionally thought to indicate good fit) can actually indicate poor fit with poor measurement quality (e.g., standardized factors loadings of around 0.40). Conversely, an RMSEA value of 0.20 (conventionally thought to indicate very poor fit) can indicate acceptable fit with very high measurement quality (standardized factor loadings around 0.90). Despite the wide-ranging effect on applications of latent variable models, the high level of technical detail involved with this phenomenon has curtailed the exposure of these important findings to empirical researchers who are employing these methods. This article briefly reviews these methodological studies in minimal technical detail and provides a demonstration to easily quantify the large influence measurement quality has on fit index values and how greatly the cutoffs would change if they were derived under an alternative level of measurement quality. Recommendations for best practice are also discussed.

  2. DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

    PubMed Central

    Peng, Shengwen; You, Ronghui; Wang, Hongning; Zhai, Chengxiang; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2016-01-01

    Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. Availability and Implementation: The software is available upon request. Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307646

  3. DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

    PubMed

    Peng, Shengwen; You, Ronghui; Wang, Hongning; Zhai, Chengxiang; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2016-06-15

    Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the 'learning to rank' framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. The software is available upon request. zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  4. Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis.

    PubMed

    Pallez, Denis; Gardès, Julien; Pasquier, Claude

    2017-09-05

    MicroRNAs, small non-coding elements implied in gene regulation, are very interesting biomarkers for various diseases such as cancers. They represent potential prodigious biotechnologies for early diagnosis and gene therapies. However, experimental verification of microRNA-disease associations are time-consuming and costly, so that computational modeling is a proper solution. Previously, we designed MiRAI, a predictive method based on distributional semantics, to identify new associations between microRNA molecules and human diseases. Our preliminary results showed very good prediction scores compared to other available methods. However, MiRAI performances depend on numerous parameters that cannot be tuned manually. In this study, a parallel evolutionary algorithm is proposed for finding an optimal configuration of our predictive method. The automatically parametrized version of MiRAI achieved excellent performance. It highlighted new miRNA-disease associations, especially the potential implication of mir-188 and mir-795 in various diseases. In addition, our method allowed to detect several putative false associations contained in the reference database.

  5. Journal descriptor indexing tool for categorizing text according to discipline or semantic type.

    PubMed

    Humphrey, Susanne M; Lu, Chris J; Rogers, Willie J; Browne, Allen C

    2006-01-01

    A JDI (Journal Descriptor Indexing) tool has been developed at NLM that automatically categorizes biomedical text as input, returning a ranked list, with scores between 0-1, of either JDs (Journal Descriptors, corresponding to biomedical disciplines) or STs (UMLS Semantic Types). Possible applications include WSD (Word Sense Disambiguation) and retrieval according to discipline. The Lexical Systems Group plans to distribute an open source JAVA version of this tool.

  6. Semantic extraction and processing of medical records for patient-oriented visual index

    NASA Astrophysics Data System (ADS)

    Zheng, Weilin; Dong, Wenjie; Chen, Xiangjiao; Zhang, Jianguo

    2012-02-01

    To have comprehensive and completed understanding healthcare status of a patient, doctors need to search patient medical records from different healthcare information systems, such as PACS, RIS, HIS, USIS, as a reference of diagnosis and treatment decisions for the patient. However, it is time-consuming and tedious to do these procedures. In order to solve this kind of problems, we developed a patient-oriented visual index system (VIS) to use the visual technology to show health status and to retrieve the patients' examination information stored in each system with a 3D human model. In this presentation, we present a new approach about how to extract the semantic and characteristic information from the medical record systems such as RIS/USIS to create the 3D Visual Index. This approach includes following steps: (1) Building a medical characteristic semantic knowledge base; (2) Developing natural language processing (NLP) engine to perform semantic analysis and logical judgment on text-based medical records; (3) Applying the knowledge base and NLP engine on medical records to extract medical characteristics (e.g., the positive focus information), and then mapping extracted information to related organ/parts of 3D human model to create the visual index. We performed the testing procedures on 559 samples of radiological reports which include 853 focuses, and achieved 828 focuses' information. The successful rate of focus extraction is about 97.1%.

  7. Concept indexing and expansion for social multimedia websites based on semantic processing and graph analysis

    NASA Astrophysics Data System (ADS)

    Lin, Po-Chuan; Chen, Bo-Wei; Chang, Hangbae

    2016-07-01

    This study presents a human-centric technique for social video expansion based on semantic processing and graph analysis. The objective is to increase metadata of an online video and to explore related information, thereby facilitating user browsing activities. To analyze the semantic meaning of a video, shots and scenes are firstly extracted from the video on the server side. Subsequently, this study uses annotations along with ConceptNet to establish the underlying framework. Detailed metadata, including visual objects and audio events among the predefined categories, are indexed by using the proposed method. Furthermore, relevant online media associated with each category are also analyzed to enrich the existing content. With the above-mentioned information, users can easily browse and search the content according to the link analysis and its complementary knowledge. Experiments on a video dataset are conducted for evaluation. The results show that our system can achieve satisfactory performance, thereby demonstrating the feasibility of the proposed idea.

  8. An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition.

    PubMed

    Tsatsaronis, George; Balikas, Georgios; Malakasiotis, Prodromos; Partalas, Ioannis; Zschunke, Matthias; Alvers, Michael R; Weissenborn, Dirk; Krithara, Anastasia; Petridis, Sergios; Polychronopoulos, Dimitris; Almirantis, Yannis; Pavlopoulos, John; Baskiotis, Nicolas; Gallinari, Patrick; Artiéres, Thierry; Ngomo, Axel-Cyrille Ngonga; Heino, Norman; Gaussier, Eric; Barrio-Alvers, Liliana; Schroeder, Michael; Androutsopoulos, Ion; Paliouras, Georgios

    2015-04-30

    This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the "ideal

  9. 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…

  10. Inferring the semantic relationships of words within an ontology using random indexing: applications to pharmacogenomics.

    PubMed

    Percha, Bethany; Altman, Russ B

    2013-01-01

    The biomedical literature presents a uniquely challenging text mining problem. Sentences are long and complex, the subject matter is highly specialized with a distinct vocabulary, and producing annotated training data for this domain is time consuming and expensive. In this environment, unsupervised text mining methods that do not rely on annotated training data are valuable. Here we investigate the use of random indexing, an automated method for producing vector-space semantic representations of words from large, unlabeled corpora, to address the problem of term normalization in sentences describing drugs and genes. We show that random indexing produces similarity scores that capture some of the structure of PHARE, a manually curated ontology of pharmacogenomics concepts. We further show that random indexing can be used to identify likely word candidates for inclusion in the ontology, and can help localize these new labels among classes and roles within the ontology.

  11. Inferring the semantic relationships of words within an ontology using random indexing: applications to pharmacogenomics

    PubMed Central

    Percha, Bethany; Altman, Russ B.

    2013-01-01

    The biomedical literature presents a uniquely challenging text mining problem. Sentences are long and complex, the subject matter is highly specialized with a distinct vocabulary, and producing annotated training data for this domain is time consuming and expensive. In this environment, unsupervised text mining methods that do not rely on annotated training data are valuable. Here we investigate the use of random indexing, an automated method for producing vector-space semantic representations of words from large, unlabeled corpora, to address the problem of term normalization in sentences describing drugs and genes. We show that random indexing produces similarity scores that capture some of the structure of PHARE, a manually curated ontology of pharmacogenomics concepts. We further show that random indexing can be used to identify likely word candidates for inclusion in the ontology, and can help localize these new labels among classes and roles within the ontology. PMID:24551397

  12. BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature

    PubMed Central

    Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S.; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

    2011-01-01

    With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php. PMID:21558175

  13. BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

    PubMed

    Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

    2011-07-01

    With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php.

  14. Trajectories of body mass index before the diagnosis of cardiovascular disease: a latent class trajectory analysis.

    PubMed

    Dhana, Klodian; van Rosmalen, Joost; Vistisen, Dorte; Ikram, M Arfan; Hofman, Albert; Franco, Oscar H; Kavousi, Maryam

    2016-06-01

    Patients with cardiovascular disease (CVD) are a heterogeneous group regarding their body mass index (BMI) levels at the time of diagnosis. To address the heterogeneity of CVD, we examined the trajectories of change in body mass index (BMI) and in other cardio-metabolic risk factors before CVD diagnosis. The study included 6126 participants from the prospective population-based Rotterdam Study, followed over 22 years with clinical examinations every 4 years. Latent class trajectory analysis and mixed-effect models were used to develop trajectories of BMI and other cardio-metabolic risk factors respectively. During follow-up, 1748 participants developed CVD, among whom we identified 3 distinct BMI trajectories. The majority of participants (n = 1534, 87.8 %) had steady BMI levels during follow-up, comprising the "stable weight" group. This group showed decrease in mean high-density lipoprotein (HDL) cholesterol over time. The second group, the "progressive weight gain" group (n = 112, 6.4 %), showed a progressive increase in BMI levels. In this group, mean waist circumference increased, mean HDL cholesterol decreased and mean fasting glucose levels were fluctuating over follow-up. In the third group, the "progressive weight loss" group (n = 102, 5.8 %), BMI levels decreased during follow-up. This group showed a decrease in mean waist circumference and in fasting glucose. In conclusion, the majority of individuals who developed CVD had a stable weight during follow-up, suggesting that BMI alone is not a good indicator for identifying middle-aged and elderly individuals at high risk of CVD. Waist circumference, HDL cholesterol, and glucose trajectories differed between the identified BMI subgroups, further highlighting that CVD is a heterogeneous disease with different pathophysiological pathways.

  15. Visualizing Semantic Spaces and Author Co-citation Networks in Digital Libraries.

    ERIC Educational Resources Information Center

    Chen, Chaomei

    1999-01-01

    Describes the development and application of visualization techniques for users to access and explore information in digital libraries effectively and intuitively. Salient semantic structures and citation patterns are extracted from several collections of documents using Latent Semantic Indexing and Pathfinder Network Scaling. Author cocitation…

  16. The Nature of Indexing: How Humans and Machines Analyze Messages and Texts for Retrieval. Part II: Machine Indexing, and the Allocation of Human versus Machine Effort.

    ERIC Educational Resources Information Center

    Anderson, James D.; Perez-Carballo, Jose

    2001-01-01

    Discussion of human intellectual indexing versus automatic indexing focuses on automatic indexing. Topics include keyword indexing; negative vocabulary control; counting words; comparative counting and weighting; stemming; words versus phrases; clustering; latent semantic indexing; citation indexes; bibliographic coupling; co-citation; relevance…

  17. The Nature of Indexing: How Humans and Machines Analyze Messages and Texts for Retrieval. Part II: Machine Indexing, and the Allocation of Human versus Machine Effort.

    ERIC Educational Resources Information Center

    Anderson, James D.; Perez-Carballo, Jose

    2001-01-01

    Discussion of human intellectual indexing versus automatic indexing focuses on automatic indexing. Topics include keyword indexing; negative vocabulary control; counting words; comparative counting and weighting; stemming; words versus phrases; clustering; latent semantic indexing; citation indexes; bibliographic coupling; co-citation; relevance…

  18. A neural signature of food semantics is associated with body-mass index.

    PubMed

    Pergola, Giulio; Foroni, Francesco; Mengotti, Paola; Argiris, Georgette; Rumiati, Raffaella Ida

    2017-09-09

    Visual recognition of objects may rely on different features depending on the category to which they belong. Recognizing natural objects, such as fruits and plants, weighs more on their perceptual attributes, whereas recognizing man-made objects, such as tools or vehicles, weighs more upon the functions and actions they enable. Edible objects are perceptually rich but also prepared for specific functions, therefore it is unclear how perceptual and functional attributes affect their recognition. Two event-related potentials experiments investigated: (i) whether food categorization in the brain is differentially modulated by sensory and functional attributes, depending on whether the food is natural or transformed; (ii) whether these processes are modulated by participants' body mass index. In experiment 1, healthy normal-weight participants were presented with a sentence (prime) and a photograph of a food. Primes described either a sensory feature ('It tastes sweet') or a functional feature ('It is suitable for a wedding party') of the food, while photographs depicted either a natural (e.g., cherry) or a transformed food (e.g., pizza). Prime-feature pairs were either congruent or incongruent. This design aimed at modulating N400-like components elicited by semantic processing. In experiment 1, N400-like amplitude was significantly larger for transformed food than for natural food with sensory primes, and vice versa with functional primes. In experiment 2, underweight and obese women performed the same semantic task. We found that, while the N400-like component in obese participants was modulated by sensory-functional primes only for transformed food, the same modulation was found in underweight participants only for natural food. These findings suggest that the level of food transformation interacts with participants' body mass index in modulating food perception and the underlying brain processing. Crown Copyright © 2017. Published by Elsevier B.V. All rights

  19. Targeted Echocardiographic Screening for Latent Rheumatic Heart Disease in Northern Uganda: Evaluating Familial Risk Following Identification of an Index Case

    PubMed Central

    Aliku, Twalib; Sable, Craig; Scheel, Amy; Tompsett, Alison; Lwabi, Peter; Okello, Emmy; McCarter, Robert; Summar, Marshall; Beaton, Andrea

    2016-01-01

    Background Echocardiographic screening for detection of latent RHD has shown potential as a strategy to decrease the burden of disease. However, further research is needed to determine optimal implementation strategies. RHD results from a complex interplay between environment and host susceptibility. Family members share both and relatives of children with latent RHD may represent a high-risk group. The objective of this study was to use echocardiographic family screening to determine the relative risk of RHD among first-degree relatives of children with latent RHD compared to the risk in first-degree relatives of healthy peers. Methodology/Principal Findings Previous school-based screening data were used to identify RHD positive children and RHD negative peers. All first-degree relatives ≥ 5 years were invited for echocardiography screening (2012 World Heart Federation Criteria). Sixty RHD positive cases (30 borderline/30 definite RHD) and 67 RHD negative cases were recruited. A total of 455/667 (68%) family members were screened. Definite RHD was more common in childhood siblings of RHD positive compared to RHD negative (p = 0.05). Children with any RHD were 4.5 times as likely to have a sibling with definite RHD, a risk that increased to 5.6 times when considering only cases with definite RHD. Mothers of RHD positive and RHD negative cases had an unexpectedly high rate of latent RHD (9.3%). Conclusions/Significance Siblings of RHD positive cases with RHD are more likely to have definite RHD and the relative risk is highest if the index case has definite RHD. Future screening programs should consider implementation of sibling screening following detection of an RHD positive child. Larger screening studies of adults are needed, as data on prevalence of latent RHD outside of childhood are sparse. Future studies should prioritize implementation research to answer questions of how RHD screening can best be integrated into existing healthcare structures, ensuring

  20. A Semantic Analysis of Abstracts Around an Experiment in Mechanized Indexing.

    ERIC Educational Resources Information Center

    Noel, Jacques

    The first part of this dissertation is a metatheoretical discussion of the needs and means of semantic analysis. This discussion includes sections on metalanguage, deep and surface structure and structural semantics, and procedures for relating the English of abstracts to a classification concordance in the same language. The second part describes…

  1. Word Sense Disambiguation by Selecting the Best Semantic Type Based on Journal Descriptor Indexing: Preliminary Experiment.

    PubMed

    Humphrey, Susanne M; Rogers, Willie J; Kilicoglu, Halil; Demner-Fushman, Dina; Rindflesch, Thomas C

    2006-01-01

    An experiment was performed at the National Library of Medicine((R)) (NLM((R))) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM's MetaMap system, which maps free text to terms corresponding to concepts in NLM's Unified Medical Language System((R)) (UMLS((R))) Metathesaurus((R)). If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based on statistical associations between words in a training set of MEDLINE((R)) citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the basis for selecting the best meaning that is correlated to UMLS semantic types (STs) assigned to ambiguous concepts in the Metathesaurus. For example, the ambiguity transport has two meanings: "Biological Transport" assigned the ST Cell Function and "Patient transport" assigned the ST Health Care Activity. A JDI-based methodology can analyze text containing transport and determine which ST receives a higher score for that text, which then returns the associated meaning, presumed to apply to the ambiguity itself. We then present an experiment in which a baseline disambiguation method was compared to four versions of JDI in disambiguating 45 ambiguous strings from NLM's WSD Test Collection. Overall average precision for the highest-scoring JDI version was 0.7873 compared to 0.2492 for the baseline method, and average precision for individual ambiguities was greater than 0.90 for 23 of them (51%), greater than 0.85 for 24 (53%), and greater than 0.65 for 35 (79%). On the basis of these results, we hope to improve performance of JDI and test its use in applications.

  2. Nouns, verbs, objects, actions, and abstractions: Local fMRI activity indexes semantics, not lexical categories

    PubMed Central

    Moseley, Rachel L.; Pulvermüller, Friedemann

    2014-01-01

    Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions. Abstract words, pertaining to neither, are a critical test case: dissociations along lexical-grammatical lines would support models purporting lexical category as the principle governing brain organisation, whilst semantic models predict dissociation between concrete words but not abstract items. During fMRI scanning, participants read orthogonalised word categories of nouns and verbs, with or without concrete, sensorimotor meaning. Analysis of inferior frontal/insula, precentral and central areas revealed an interaction between lexical class and semantic factors with clear category differences between concrete nouns and verbs but not abstract ones. Though the brain stores the combinatorial and lexical-grammatical properties of words, our data show that topographical differences in brain activation, especially in the motor system and inferior frontal cortex, are driven by semantics and not by lexical class. PMID:24727103

  3. Nouns, verbs, objects, actions, and abstractions: local fMRI activity indexes semantics, not lexical categories.

    PubMed

    Moseley, Rachel L; Pulvermüller, Friedemann

    2014-05-01

    Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions. Abstract words, pertaining to neither, are a critical test case: dissociations along lexical-grammatical lines would support models purporting lexical category as the principle governing brain organisation, whilst semantic models predict dissociation between concrete words but not abstract items. During fMRI scanning, participants read orthogonalised word categories of nouns and verbs, with or without concrete, sensorimotor meaning. Analysis of inferior frontal/insula, precentral and central areas revealed an interaction between lexical class and semantic factors with clear category differences between concrete nouns and verbs but not abstract ones. Though the brain stores the combinatorial and lexical-grammatical properties of words, our data show that topographical differences in brain activation, especially in the motor system and inferior frontal cortex, are driven by semantics and not by lexical class.

  4. Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

    PubMed

    Uthayan, K R; Mala, G S Anandha

    2015-01-01

    Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

  5. Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models.

    PubMed

    Papanikolaou, Yannis; Tsoumakas, Grigorios; Laliotis, Manos; Markantonatos, Nikos; Vlahavas, Ioannis

    2017-09-22

    In this paper we present the approach that we employed to deal with large scale multi-label semantic indexing of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge (2013-2017), a challenge concerned with biomedical semantic indexing and question answering. Our main contribution is a MUlti-Label Ensemble method (MULE) that incorporates a McNemar statistical significance test in order to validate the combination of the constituent machine learning algorithms. Some secondary contributions include a study on the temporal aspects of the BioASQ corpus (observations apply also to the BioASQ's super-set, the PubMed articles collection) and the proper parametrization of the algorithms used to deal with this challenging classification task. The ensemble method that we developed is compared to other approaches in experimental scenarios with subsets of the BioASQ corpus giving positive results. In our participation in the BioASQ challenge we obtained the first place in 2013 and the second place in the four following years, steadily outperforming MTI, the indexing system of the National Library of Medicine (NLM). The results of our experimental comparisons, suggest that employing a statistical significance test to validate the ensemble method's choices, is the optimal approach for ensembling multi-label classifiers, especially in contexts with many rare labels.

  6. Analysis of semantic search within the domains of uncertainty: using Keyword Effectiveness Indexing as an evaluation tool.

    PubMed

    Lorence, Daniel; Abraham, Joanna

    2006-01-01

    Medical and health-related searches pose a special case of risk when using the web as an information resource. Uninsured consumers, lacking access to a trained provider, will often rely on information from the internet for self-diagnosis and treatment. In areas where treatments are uncertain or controversial, most consumers lack the knowledge to make an informed decision. This exploratory technology assessment examines the use of Keyword Effectiveness Indexing (KEI) analysis as a potential tool for profiling information search and keyword retrieval patterns. Results demonstrate that the KEI methodology can be useful in identifying e-health search patterns, but is limited by semantic or text-based web environments.

  7. VastMM-Tag: Semantic Indexing and Browsing of Videos for E-Learning

    ERIC Educational Resources Information Center

    Morris, Mitchell J.

    2012-01-01

    Quickly accessing the contents of a video is challenging for users, particularly for unstructured video, which contains no intentional shot boundaries, no chapters, and no apparent edited format. We approach this problem in the domain of lecture videos though the use of machine learning, to gather semantic information about the videos; and through…

  8. Dissociation of Event-Related Potentials Indexing Arousal and Semantic Cohesion During Emotional Word Encoding

    ERIC Educational Resources Information Center

    Dillon, Daniel G.; Cooper, Julie J.; Grent-'t-Jong, Tineke; Woldorff, Marty G.; LaBar, Kevin S.

    2006-01-01

    Event-related potential (ERP) studies have shown that emotional stimuli elicit greater amplitude late positive-polarity potentials (LPPs) than neutral stimuli. This effect has been attributed to arousal, but emotional stimuli are also more semantically coherent than uncategorized neutral stimuli. ERPs were recorded during encoding of positive,…

  9. VastMM-Tag: Semantic Indexing and Browsing of Videos for E-Learning

    ERIC Educational Resources Information Center

    Morris, Mitchell J.

    2012-01-01

    Quickly accessing the contents of a video is challenging for users, particularly for unstructured video, which contains no intentional shot boundaries, no chapters, and no apparent edited format. We approach this problem in the domain of lecture videos though the use of machine learning, to gather semantic information about the videos; and through…

  10. A Media Agent for Automatically Building a Personalized Semantic Index of Web Media Objects.

    ERIC Educational Resources Information Center

    Wenyin, Liu; Chen, Zheng; Li, Mingjing; Zhang, Hongjiang

    2001-01-01

    Describes a multimedia approach to solving image retrieval problems. Presents the idea of a media agent, which can automatically collect semantic descriptions of multimedia data into a personal media database on behalf of a particular user whenever and wherever he/she accesses these multimedia data. Discusses implementation and applications of the…

  11. GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

    PubMed Central

    Shyu, Chi-Ren; Klaric, Matt; Scott, Grant J.; Barb, Adrian S.; Davis, Curt H.; Palaniappan, Kannappan

    2007-01-01

    Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts’ questions in seconds, such as “given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark.” PMID:18270555

  12. GeoIRIS: Geospatial Information Retrieval and Indexing System-Content Mining, Semantics Modeling, and Complex Queries.

    PubMed

    Shyu, Chi-Ren; Klaric, Matt; Scott, Grant J; Barb, Adrian S; Davis, Curt H; Palaniappan, Kannappan

    2007-04-01

    Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts' questions in seconds, such as "given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark."

  13. Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record.

    PubMed

    Lowe, H J; Antipov, I; Hersh, W; Smith, C A; Mailhot, M

    1999-12-01

    This paper describes preliminary work evaluating automated semantic indexing of radiology imaging reports to represent images stored in the Image Engine multimedia medical record system at the University of Pittsburgh Medical Center. The authors used the SAPHIRE indexing system to automatically identify important biomedical concepts within radiology reports and represent these concepts with terms from the 1998 edition of the U.S. National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus. This automated UMLS indexing was then compared with manual UMLS indexing of the same reports. Human indexing identified appropriate UMLS Metathesaurus descriptors for 81% of the important biomedical concepts contained in the report set. SAPHIRE automatically identified UMLS Metathesaurus descriptors for 64% of the important biomedical concepts contained in the report set. The overall conclusions of this pilot study were that the UMLS metathesaurus provided adequate coverage of the majority of the important concepts contained within the radiology report test set and that SAPHIRE could automatically identify and translate almost two thirds of these concepts into appropriate UMLS descriptors. Further work is required to improve both the recall and precision of this automated concept extraction process.

  14. Cross-language MeSH indexing using morpho-semantic normalization.

    PubMed

    Markó, Kornél; Daumke, Philipp; Schulz, Stefan; Hahn, Udo

    2003-01-01

    We consider three alternative procedures for the automatic indexing of medical documents using MeSH thesaurus identifiers as target units (document descriptors). Rather than considering complete words as the starting point of the indexing procedure, we here propose morphologically plausible subwords as basic units from which MeSH terms are derived. We describe the morphological segmentation and normalization procedures, as well as the mappings from subwords to MeSH terms, and discuss results from an evaluation carried out on a German-language corpus.

  15. Cross-language MeSH Indexing using Morpho-Semantic Normalization

    PubMed Central

    Markó, Kornél; Daumke, Philipp; Schulz, Stefan; Hahn, Udo

    2003-01-01

    We consider three alternative procedures for the automatic indexing of medical documents using MeSH thesaurus identifiers as target units (document descriptors). Rather than considering complete words as the starting point of the indexing procedure, we here propose morphologically plausible subwords as basic units from which MeSH terms are derived. We describe the morphological segmentation and normalization procedures, as well as the mappings from sub-words to MeSH terms, and discuss results from an evaluation carried out on a German-language corpus. PMID:14728208

  16. Indexing method of digital audiovisual medical resources with semantic Web integration.

    PubMed

    Cuggia, Marc; Mougin, Fleur; Le Beux, Pierre

    2003-01-01

    Digitalization of audio-visual resources combined with the performances of the networks offer many possibilities which are the subject of intensive work in the scientific and industrial sectors. Indexing such resources is a major challenge. Recently, the Motion Pictures Expert Group (MPEG) has been developing MPEG-7, a standard for describing multimedia content. The good of this standard is to develop a rich set of standardized tools to enable fast efficient retrieval from digital archives or filtering audiovisual broadcasts on the internet. How this kind of technologies could be used in the medical context? In this paper, we propose a simpler indexing system, based on Dublin Core standard and complaint to MPEG-7. We use MeSH and UMLS to introduce conceptual navigation. We also present a video-platform with enables to encode and give access to audio-visual resources in streaming mode.

  17. Indexing method of digital audiovisual medical resources with semantic Web integration.

    PubMed

    Cuggia, Marc; Mougin, Fleur; Le Beux, Pierre

    2005-03-01

    Digitalization of audiovisual resources and network capability offer many possibilities which are the subject of intensive work in scientific and industrial sectors. Indexing such resources is a major challenge. Recently, the Motion Pictures Expert Group (MPEG) has developed MPEG-7, a standard for describing multimedia content. The goal of this standard is to develop a rich set of standardized tools to enable efficient retrieval from digital archives or the filtering of audiovisual broadcasts on the Internet. How could this kind of technology be used in the medical context? In this paper, we propose a simpler indexing system, based on the Dublin Core standard and compliant to MPEG-7. We use MeSH and the UMLS to introduce conceptual navigation. We also present a video-platform which enables encoding and gives access to audiovisual resources in streaming mode.

  18. Automated Semantic Indexing of Figure Captions to Improve Radiology Image Retrieval

    PubMed Central

    Kahn, Charles E.; Rubin, Daniel L.

    2009-01-01

    Objective We explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals. Design The MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval. Measurements Precision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine. Results Estimated precision was 0.897 (95% confidence interval, 0.857–0.937). Estimated recall was 0.930 (95% confidence interval, 0.838–1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone. Conclusion Concept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval. PMID:19261938

  19. Automated semantic indexing of figure captions to improve radiology image retrieval.

    PubMed

    Kahn, Charles E; Rubin, Daniel L

    2009-01-01

    We explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals. The MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval. Precision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine. Estimated precision was 0.897 (95% confidence interval, 0.857-0.937). Estimated recall was 0.930 (95% confidence interval, 0.838-1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone. Concept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval.

  20. Once is Enough: N400 Indexes Semantic Integration of Novel Word Meanings from a Single Exposure in Context

    PubMed Central

    Borovsky, Arielle; Elman, Jeffrey L.; Kutas, Marta

    2012-01-01

    We investigated the impact of contextual constraint on the integration of novel word meanings into semantic memory. Adults read strongly or weakly constraining sentences ending in known or unknown (novel) words as scalp-recorded electrical brain activity was recorded. Word knowledge was assessed via a lexical decision task in which recently seen known and unknown word sentence endings served as primes for semantically related, unrelated, and synonym/identical target words. As expected, N400 amplitudes to target words preceded by known word primes were reduced by prime-target relatedness. Critically, N400 amplitudes to targets preceded by novel primes also varied with prime-target relatedness, but only when they had initially appeared in highly constraining sentences. This demonstrates for the first time that fast-mapped word representations can develop strong associations with semantically related word meanings and reveals a rapid neural process that can integrate information about word meanings into the mental lexicon of young adults. PMID:23125559

  1. Once Is Enough: N400 Indexes Semantic Integration of Novel Word Meanings from a Single Exposure in Context

    ERIC Educational Resources Information Center

    Borovsky, Arielle; Elman, Jeffrey L.; Kutas, Marta

    2012-01-01

    We investigated the impact of contextual constraint on the integration of novel word meanings into semantic memory. Adults read strongly or weakly constraining sentences ending in known or unknown (novel) words as scalp-recorded electrical brain activity was recorded. Word knowledge was assessed via a lexical decision task in which recently seen…

  2. Once Is Enough: N400 Indexes Semantic Integration of Novel Word Meanings from a Single Exposure in Context

    ERIC Educational Resources Information Center

    Borovsky, Arielle; Elman, Jeffrey L.; Kutas, Marta

    2012-01-01

    We investigated the impact of contextual constraint on the integration of novel word meanings into semantic memory. Adults read strongly or weakly constraining sentences ending in known or unknown (novel) words as scalp-recorded electrical brain activity was recorded. Word knowledge was assessed via a lexical decision task in which recently seen…

  3. Semantic and Social Networks Comparison for the Haiti Earthquake Relief Operations from APAN Data Sources using Lexical Link Analysis (LLA)

    DTIC Science & Technology

    2012-06-01

    Semantic Analysis (LSA; (Dumais, Furnas, Landauer, Deerwester, & Harshman, 1988; Gorman, 2003; Letsche, 1997) and Probabilistic Latent Semantic Analysis...Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R. (1988). Using latent semantic analysis to improve information retrieval...analysis/ Gorman, J. C., Foltz, P. W. Kiekel, P. A., Martin, M. A. & Cooke, N. J. (2003) Evaluation of Latent Semantic Analysis-based measures of

  4. Semantic Tools in Information Retrieval.

    ERIC Educational Resources Information Center

    Rubinoff, Morris; Stone, Don C.

    This report discusses the problem of the meansings of words used in information retrieval systems, and shows how semantic tools can aid in the communication which takes place between indexers and searchers via index terms. After treating the differing use of semantic tools in different types of systems, two tools (classification tables and…

  5. Memory for Narrative and Expository Text: Independent Influences of Semantic Associations and Text Organization

    ERIC Educational Resources Information Center

    Wolfe, Michael B. W.

    2005-01-01

    The author examined memory for text in terms of the independent influences of semantic knowledge associations and text organization. Semantic associations were operationalized as the semantic relatedness between individual text concepts and the text as a whole and assessed with latent semantic analysis. The author assessed text organization by…

  6. Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

    ERIC Educational Resources Information Center

    Anaya, Leticia H.

    2011-01-01

    In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

  7. Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

    ERIC Educational Resources Information Center

    Anaya, Leticia H.

    2011-01-01

    In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

  8. Semantic Data Matching: Principles and Performance

    NASA Astrophysics Data System (ADS)

    Deaton, Russell; Doan, Thao; Schweiger, Tom

    Automated and real-time management of customer relationships requires robust and intelligent data matching across widespread and diverse data sources. Simple string matching algorithms, such as dynamic programming, can handle typographical errors in the data, but are less able to match records that require contextual and experiential knowledge. Latent Semantic Indexing (LSI) (Berry et al. ; Deerwester et al. is a machine intelligence technique that can match data based upon higher order structure, and is able to handle difficult problems, such as words that have different meanings but the same spelling, are synonymous, or have multiple meanings. Essentially, the technique matches records based upon context, or mathematically quantifying when terms occur in the same record.

  9. A Semantic Relatedness Approach for Traceability Link Recovery

    SciTech Connect

    Mahmoud, Anas M.; Niu, Nan; Xu, Songhua

    2012-01-01

    Human analysts working with automated tracing tools need to directly vet candidate traceability links in order to determine the true traceability information. Currently, human intervention happens at the end of the traceability process, after candidate traceability links have already been generated. This often leads to a decline in the results accuracy. In this paper, we propose an approach, based on semantic relatedness (SR), which brings human judgment to an earlier stage of the tracing process by integrating it into the underlying retrieval mechanism. SR tries to mimic human mental model of relevance by considering a broad range of semantic relations, hence producing more semantically meaningful results. We evaluated our approach using three datasets from different application domains, and assessed the tracing results via six different performance measures concerning both result quality and browsability. The empirical evaluation results show that our SR approach achieves a significantly better performance in recovering true links than a standard Vector Space Model (VSM) in all datasets. Our approach also achieves a significantly better precision than Latent Semantic Indexing (LSI) in two of our datasets.

  10. Generative Semantics.

    ERIC Educational Resources Information Center

    King, Margaret

    The first section of this paper deals with the attempts within the framework of transformational grammar to make semantics a systematic part of linguistic description, and outlines the characteristics of the generative semantics position. The second section takes a critical look at generative semantics in its later manifestations, and makes a case…

  11. Semantic Processing of Mathematical Gestures

    ERIC Educational Resources Information Center

    Lim, Vanessa K.; Wilson, Anna J.; Hamm, Jeff P.; Phillips, Nicola; Iwabuchi, Sarina J.; Corballis, Michael C.; Arzarello, Ferdinando; Thomas, Michael O. J.

    2009-01-01

    Objective: To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect. Results: The N400 effect elicited by words primed with mathematical gestures…

  12. Semantic Processing of Mathematical Gestures

    ERIC Educational Resources Information Center

    Lim, Vanessa K.; Wilson, Anna J.; Hamm, Jeff P.; Phillips, Nicola; Iwabuchi, Sarina J.; Corballis, Michael C.; Arzarello, Ferdinando; Thomas, Michael O. J.

    2009-01-01

    Objective: To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect. Results: The N400 effect elicited by words primed with mathematical gestures…

  13. Longitudinal associations between body mass index, physical activity, and healthy dietary behaviors in adults: A parallel latent growth curve modeling approach

    PubMed Central

    Kim, Youngdeok; Lee, Jung-Min; Kim, Jungyoon; Dhurandhar, Emily; Soliman, Ghada; Wehbi, Nizar K.; Canedy, James

    2017-01-01

    Background Physical activity (PA) and healthy dietary behaviors (HDB) are two well-documented lifestyle factors influencing body mass index (BMI). This study examined 7-year longitudinal associations between changes in PA, HDB, and BMI among adults using a parallel latent growth curve modeling (LGCM). Methods We used prospective cohort data collected by a private company (SimplyWell LLC, Omaha, NE, USA) implementing a workplace health screening program. Data from a total of 2,579 adults who provided valid BMI, PA, and HDB information for at least 5 out of 7 follow-up years from the time they entered the program were analyzed. PA and HDB were subjectively measured during an annual online health survey. Height and weight measured during an annual onsite health screening were used to calculate BMI (kg·m2). The parallel LGCMs stratified by gender and baseline weight status (normal: BMI<25, overweight BMI 25–29.9, and obese: BMI>30) were fitted to examine the longitudinal associations of changes in PA and HDB with change in BMI over years. Results On average, BMI gradually increased over years, at rates ranging from 0.06 to 0.20 kg·m2·year, with larger increases observed among those of normal baseline weight status across genders. The increases in PA and HDB were independently associated with a smaller increase in BMI for obese males (b = -1.70 and -1.98, respectively), and overweight females (b = -1.85 and -2.46, respectively) and obese females (b = -2.78 and -3.08, respectively). However, no significant associations of baseline PA and HDB with changes in BMI were observed. Conclusions Our study suggests that gradual increases in PA and HDB are independently associated with smaller increases in BMI in overweight and obese adults, but not in normal weight individuals. Further study is warranted to address factors that check increases in BMI in normal weight adults. PMID:28296945

  14. Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval.

    PubMed

    Lowe, H J; Antipov, I; Hersh, W; Smith, C A

    1998-01-01

    Medicine is increasingly image-intensive. The central importance of imaging technologies such as computerized tomography and magnetic resonance imaging in clinical decision making, combined with the trend to store many "traditional" clinical images such as conventional radiographs, microscopic pathology and dermatology images in digital format present both challenges and an opportunities for the designers of clinical information systems. The emergence of Multimedia Electronic Medical Record Systems (MEMRS), architectures that integrate medical images with text-based clinical data, will further hasten this trend. The development of these systems, storing a large and diverse set of medical images, suggests that in the future MEMRS will become important digital libraries supporting patient care, research and education. The representation and retrieval of clinical images within these systems is problematic as conventional database architectures and information retrieval models have, until recently, focused largely on text-based data. Medical imaging data differs in many ways from text-based medical data but perhaps the most important difference is that the information contained within imaging data is fundamentally knowledge-based. New representational and retrieval models for clinical images will be required to address this issue. Within the Image Engine multimedia medical record system project at the University of Pittsburgh we are evolving an approach to representation and retrieval of medical images which combines semantic indexing using the UMLS Metathesuarus, image content-based representation and knowledge-based image analysis.

  15. Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval.

    PubMed Central

    Lowe, H. J.; Antipov, I.; Hersh, W.; Smith, C. A.

    1998-01-01

    Medicine is increasingly image-intensive. The central importance of imaging technologies such as computerized tomography and magnetic resonance imaging in clinical decision making, combined with the trend to store many "traditional" clinical images such as conventional radiographs, microscopic pathology and dermatology images in digital format present both challenges and an opportunities for the designers of clinical information systems. The emergence of Multimedia Electronic Medical Record Systems (MEMRS), architectures that integrate medical images with text-based clinical data, will further hasten this trend. The development of these systems, storing a large and diverse set of medical images, suggests that in the future MEMRS will become important digital libraries supporting patient care, research and education. The representation and retrieval of clinical images within these systems is problematic as conventional database architectures and information retrieval models have, until recently, focused largely on text-based data. Medical imaging data differs in many ways from text-based medical data but perhaps the most important difference is that the information contained within imaging data is fundamentally knowledge-based. New representational and retrieval models for clinical images will be required to address this issue. Within the Image Engine multimedia medical record system project at the University of Pittsburgh we are evolving an approach to representation and retrieval of medical images which combines semantic indexing using the UMLS Metathesuarus, image content-based representation and knowledge-based image analysis. PMID:9929345

  16. Learning multimodal latent attributes.

    PubMed

    Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

    2014-02-01

    The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular, we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multimodal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we 1) introduce a concept of semilatent attribute space, expressing user-defined and latent attributes in a unified framework, and 2) propose a novel scalable probabilistic topic model for learning multimodal semilatent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multitask learning, learning with label noise, N-shot transfer learning, and importantly zero-shot learning.

  17. 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…

  18. 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…

  19. Language networks associated with computerized semantic indices.

    PubMed

    Pakhomov, Serguei V S; Jones, David T; Knopman, David S

    2015-01-01

    Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory.

  20. Language Networks Associated with Computerized Semantic Indices

    PubMed Central

    Pakhomov, Serguei V. S.; Jones, David T.; Knopman, David S.

    2014-01-01

    Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. PMID:25315785

  1. Semantic Desktop

    NASA Astrophysics Data System (ADS)

    Sauermann, Leo; Kiesel, Malte; Schumacher, Kinga; Bernardi, Ansgar

    In diesem Beitrag wird gezeigt, wie der Arbeitsplatz der Zukunft aussehen könnte und wo das Semantic Web neue Möglichkeiten eröffnet. Dazu werden Ansätze aus dem Bereich Semantic Web, Knowledge Representation, Desktop-Anwendungen und Visualisierung vorgestellt, die es uns ermöglichen, die bestehenden Daten eines Benutzers neu zu interpretieren und zu verwenden. Dabei bringt die Kombination von Semantic Web und Desktop Computern besondere Vorteile - ein Paradigma, das unter dem Titel Semantic Desktop bekannt ist. Die beschriebenen Möglichkeiten der Applikationsintegration sind aber nicht auf den Desktop beschränkt, sondern können genauso in Web-Anwendungen Verwendung finden.

  2. Embedding of semantic predications.

    PubMed

    Cohen, Trevor; Widdows, Dominic

    2017-04-01

    This paper concerns the generation of distributed vector representations of biomedical concepts from structured knowledge, in the form of subject-relation-object triplets known as semantic predications. Specifically, we evaluate the extent to which a representational approach we have developed for this purpose previously, known as Predication-based Semantic Indexing (PSI), might benefit from insights gleaned from neural-probabilistic language models, which have enjoyed a surge in popularity in recent years as a means to generate distributed vector representations of terms from free text. To do so, we develop a novel neural-probabilistic approach to encoding predications, called Embedding of Semantic Predications (ESP), by adapting aspects of the Skipgram with Negative Sampling (SGNS) algorithm to this purpose. We compare ESP and PSI across a number of tasks including recovery of encoded information, estimation of semantic similarity and relatedness, and identification of potentially therapeutic and harmful relationships using both analogical retrieval and supervised learning. We find advantages for ESP in some, but not all of these tasks, revealing the contexts in which the additional computational work of neural-probabilistic modeling is justified.

  3. Capturing the Semantics of User Interaction: A Review and Case Study

    NASA Astrophysics Data System (ADS)

    Morrison, Donn; Marchand-Maillet, Stéphane; Bruno, Eric

    In many retrieval domains there exists a problematic gap between what computers can describe and what humans are capable of perceiving. This gap is most evident in the indexing of multimedia data such as images, video and sound where the low-level features are too semantically deficient to be of use from a typical users' perspective. On the other hand, users possess the ability to quickly examine and summarise these documents, even subconsciously. Examples include specifying relevance between a query and results, rating preferences in film databases, purchasing items from online retailers, and even browsing web sites. Data from these interactions, captured and stored in log files, can be interpreted to have semantic meaning, which proves indispensable when used in a collaborative setting where users share similar preferences or goals. In this chapter we summarise techniques for efficiently exploiting user interaction in its many forms for the generation and augmentation of semantic data in large databases. This user interaction can be applied to improve performance in recommender and information retrieval systems. A case study is presented which applies a popular technique, latent semantic analysis, to improve retrieval on an image database.

  4. Generative Semantics

    ERIC Educational Resources Information Center

    Bagha, Karim Nazari

    2011-01-01

    Generative semantics is (or perhaps was) a research program within linguistics, initiated by the work of George Lakoff, John R. Ross, Paul Postal and later McCawley. The approach developed out of transformational generative grammar in the mid 1960s, but stood largely in opposition to work by Noam Chomsky and his students. The nature and genesis of…

  5. Principal Semantic Components of Language and the Measurement of Meaning

    PubMed Central

    Samsonovic, Alexei V.; Ascoli, Giorgio A.

    2010-01-01

    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 (∼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

  6. The Use of Semantic Links in Hypertext Information Retrieval.

    ERIC Educational Resources Information Center

    Frei, H. P.; Stieger, D.

    1995-01-01

    Highlights semantic links and shows how the semantic content of hypertext links can be used for information retrieval. Discussion includes indexing and retrieval algorithms that exploit link content and node content; retrieval strategies exploiting semantic links, including conventional retrieval and constrained spreading activation techniques;…

  7. Generalized Latent Trait Models.

    ERIC Educational Resources Information Center

    Moustaki, Irini; Knott, Martin

    2000-01-01

    Discusses a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. Presents a unified maximum likelihood method for estimating the parameters of the generalized latent trait model and discusses the scoring of individuals on the latent dimensions.…

  8. Supervised Semantic Classification for Nuclear Proliferation Monitoring

    SciTech Connect

    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 proliferation monitoring using high resolution remote sensing images.

  9. Predicting Novel Human Gene Ontology Annotations Using Semantic Analysis

    PubMed Central

    Done, Bogdan; Khatri, Purvesh; Done, Arina; Draghici, Sorin

    2013-01-01

    The correct interpretation of many molecular biology experiments depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are meant to act as repositories for our biological knowledge as we acquire and refine it. Hence, by definition, they are incomplete at any given time. In this paper, we describe a technique that improves our previous method for predicting novel GO annotations by extracting implicit semantic relationships between genes and functions. In this work, we use a vector space model and a number of weighting schemes in addition to our previous latent semantic indexing approach. The technique described here is able to take into consideration the hierarchical structure of the Gene Ontology (GO) and can weight differently GO terms situated at different depths. The prediction abilities of 15 different weighting schemes are compared and evaluated. Nine such schemes were previously used in other problem domains, while six of them are introduced in this paper. The best weighting scheme was a novel scheme, n2tn. Out of the top 50 functional annotations predicted using this weighting scheme, we found support in the literature for 84 percent of them, while 6 percent of the predictions were contradicted by the existing literature. For the remaining 10 percent, we did not find any relevant publications to confirm or contradict the predictions. The n2tn weighting scheme also outperformed the simple binary scheme used in our previous approach. PMID:20150671

  10. Complex Semantic Networks

    NASA Astrophysics Data System (ADS)

    Teixeira, G. M.; Aguiar, M. S. F.; Carvalho, C. F.; Dantas, D. R.; Cunha, M. V.; Morais, J. H. M.; Pereira, H. B. B.; Miranda, J. G. V.

    Verbal language is a dynamic mental process. Ideas emerge by means of the selection of words from subjective and individual characteristics throughout the oral discourse. The goal of this work is to characterize the complex network of word associations that emerge from an oral discourse from a discourse topic. Because of that, concepts of associative incidence and fidelity have been elaborated and represented the probability of occurrence of pairs of words in the same sentence in the whole oral discourse. Semantic network of words associations were constructed, where the words are represented as nodes and the edges are created when the incidence-fidelity index between pairs of words exceeds a numerical limit (0.001). Twelve oral discourses were studied. The networks generated from these oral discourses present a typical behavior of complex networks and their indices were calculated and their topologies characterized. The indices of these networks obtained from each incidence-fidelity limit exhibit a critical value in which the semantic network has maximum conceptual information and minimum residual associations. Semantic networks generated by this incidence-fidelity limit depict a pattern of hierarchical classes that represent the different contexts used in the oral discourse.

  11. Semantic processing of mathematical gestures.

    PubMed

    Lim, Vanessa K; Wilson, Anna J; Hamm, Jeff P; Phillips, Nicola; Iwabuchi, Sarina J; Corballis, Michael C; Arzarello, Ferdinando; Thomas, Michael O J

    2009-12-01

    To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect. The N400 effect elicited by words primed with mathematical gestures (e.g. "converging" and "decreasing") was the same in amplitude, latency and topography as that elicited by words primed with action gestures (e.g. drive and lift), and that for terminal words of sentences. Findings provide a within-subject demonstration that the topographies of the gesture N400 effect for both action and mathematical words are indistinguishable from that of the standard language N400 effect. This suggests that mathematical function words are processed by the general language semantic system and do not appear to involve areas involved in other mathematical concepts (e.g. numerosity).

  12. Preserved musical semantic memory in semantic dementia.

    PubMed

    Weinstein, Jessica; Koenig, Phyllis; Gunawardena, Delani; McMillan, Corey; Bonner, Michael; Grossman, Murray

    2011-02-01

    To understand the scope of semantic impairment in semantic dementia. Case study. Academic medical center. A man with semantic dementia, as demonstrated by clinical, neuropsychological, and imaging studies. Music performance and magnetic resonance imaging results. Despite profoundly impaired semantic memory for words and objects due to left temporal lobe atrophy, this semiprofessional musician was creative and expressive in demonstrating preserved musical knowledge. Long-term representations of words and objects in semantic memory may be dissociated from meaningful knowledge in other domains, such as music.

  13. Preserved Musical Semantic Memory in Semantic Dementia

    PubMed Central

    Weinstein, Jessica; Koenig, Phyllis; Gunawardena, Delani; McMillan, Corey; Bonner, Michael; Grossman, Murray

    2012-01-01

    Objective To understand the scope of semantic impairment in semantic dementia. Design Case study. Setting Academic medical center. Patient A man with semantic dementia, as demonstrated by clinical, neuropsychological, and imaging studies. Main Outcome Measures Music performance and magnetic resonance imaging results. Results Despite profoundly impaired semantic memory for words and objects due to left temporal lobe atrophy, this semiprofessional musician was creative and expressive in demonstrating preserved musical knowledge. Conclusion Long-term representations of words and objects in semantic memory may be dissociated from meaningful knowledge in other domains, such as music. PMID:21320991

  14. Latent Period of Relaxation.

    PubMed

    Kobayashi, M; Irisawa, H

    1961-10-27

    The latent period of relaxation of molluscan myocardium due to anodal current is much longer than that of contraction. Although the rate and the grade of relaxation are intimately related to both the stimulus condition and the muscle tension, the latent period of relaxation remains constant, except when the temperature of the bathing fluid is changed.

  15. A Study about Placement Support Using Semantic Similarity

    ERIC Educational Resources Information Center

    Katz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

    2014-01-01

    This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…

  16. Grounding Collaborative Learning in Semantics-Based Critiquing

    ERIC Educational Resources Information Center

    Cheung, William K.; Mørch, Anders I.; Wong, Kelvin C.; Lee, Cynthia; Liu, Jiming; Lam, Mason H.

    2007-01-01

    In this article we investigate the use of latent semantic analysis (LSA), critiquing systems, and knowledge building to support computer-based teaching of English composition. We have built and tested an English composition critiquing system that makes use of LSA to analyze student essays and compute feedback by comparing their essays with…

  17. Semantic annotation in biomedicine: the current landscape.

    PubMed

    Jovanović, Jelena; Bagheri, Ebrahim

    2017-09-22

    The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.

  18. Latent fingerprint matching.

    PubMed

    Jain, Anil K; Feng, Jianjiang

    2011-01-01

    Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.

  19. Text Influenced Molecular Indexing (TIMI): a literature database mining approach that handles text and chemistry.

    PubMed

    Singh, Suresh B; Hull, Richard D; Fluder, Eugene M

    2003-01-01

    We present an application of a novel methodology called Text Influenced Molecular Indexing (TIMI) to mine the information in the scientific literature. TIMI is an extension of two existing methodologies: (1) Latent Semantic Structure Indexing (LaSSI), a method for calculating chemical similarity using two-dimensional topological descriptors, and (2) Latent Semantic Indexing (LSI), a method for generating correlations between textual terms. The singular value decomposition (SVD) of a feature/object matrix is the fundamental mathematical operation underlying LSI, LaSSI, and TIMI and is used in the identification of associations between textual and chemical descriptors. We present the results of our studies with a database containing 11,571 PubMed/MEDLINE abstracts which show the advantages of merging textual and chemical descriptors over using either text or chemistry alone. Our work demonstrates that searching text-only databases limits retrieved documents to those that explicitly mention compounds by name in the text. Similarly, searching chemistry-only databases can only retrieve those documents that have chemical structures in them. TIMI, however, enables search and retrieval of documents with textual, chemical, and/or text- and chemistry-based queries. Thus, the TIMI system offers a powerful new approach to uncovering the contextual scientific knowledge sought by the medical research community.

  20. Latent palmprint matching.

    PubMed

    Jain, Anil K; Feng, Jianjiang

    2009-06-01

    The evidential value of palmprints in forensic applications is clear as about 30 percent of the latents recovered from crime scenes are from palms. While biometric systems for palmprint-based personal authentication in access control type of applications have been developed, they mostly deal with low-resolution (about 100 ppi) palmprints and only perform full-to-full palmprint matching. We propose a latent-to-full palmprint matching system that is needed in forensic applications. Our system deals with palmprints captured at 500 ppi (the current standard in forensic applications) or higher resolution and uses minutiae as features to be compatible with the methodology used by latent experts. Latent palmprint matching is a challenging problem because latent prints lifted at crime scenes are of poor image quality, cover only a small area of the palm, and have a complex background. Other difficulties include a large number of minutiae in full prints (about 10 times as many as fingerprints), and the presence of many creases in latents and full prints. A robust algorithm to reliably estimate the local ridge direction and frequency in palmprints is developed. This facilitates the extraction of ridge and minutiae features even in poor quality palmprints. A fixed-length minutia descriptor, MinutiaCode, is utilized to capture distinctive information around each minutia and an alignment-based minutiae matching algorithm is used to match two palmprints. Two sets of partial palmprints (150 live-scan partial palmprints and 100 latent palmprints) are matched to a background database of 10,200 full palmprints to test the proposed system. Despite the inherent difficulty of latent-to-full palmprint matching, rank-1 recognition rates of 78.7 and 69 percent, respectively, were achieved in searching live-scan partial palmprints and latent palmprints against the background database.

  1. SEMANTICS AND CRITICAL READING.

    ERIC Educational Resources Information Center

    FLANIGAN, MICHAEL C.

    PROFICIENCY IN CRITICAL READING CAN BE ACCELERATED BY MAKING STUDENTS AWARE OF VARIOUS SEMANTIC DEVICES THAT HELP CLARIFY MEANINGS AND PURPOSES. EXCERPTS FROM THE ARTICLE "TEEN-AGE CORRUPTION" FROM THE NINTH-GRADE SEMANTICS UNIT WRITTEN BY THE PROJECT ENGLISH DEMONSTRATION CENTER AT EUCLID, OHIO, ARE USED TO ILLUSTRATE HOW SEMANTICS RELATE TO…

  2. Semantics via Machine Translation

    ERIC Educational Resources Information Center

    Culhane, P. T.

    1977-01-01

    Recent experiments in machine translation have given the semantic elements of collocation in Russian more objective criteria. Soviet linguists in search of semantic relationships have attempted to devise a semantic synthesis for construction of a basic language for machine translation. One such effort is summarized. (CHK)

  3. SEMANTICS AND CRITICAL READING.

    ERIC Educational Resources Information Center

    FLANIGAN, MICHAEL C.

    PROFICIENCY IN CRITICAL READING CAN BE ACCELERATED BY MAKING STUDENTS AWARE OF VARIOUS SEMANTIC DEVICES THAT HELP CLARIFY MEANINGS AND PURPOSES. EXCERPTS FROM THE ARTICLE "TEEN-AGE CORRUPTION" FROM THE NINTH-GRADE SEMANTICS UNIT WRITTEN BY THE PROJECT ENGLISH DEMONSTRATION CENTER AT EUCLID, OHIO, ARE USED TO ILLUSTRATE HOW SEMANTICS RELATE TO…

  4. Thalamic semantic paralexia

    PubMed Central

    Hoffmann, Michael

    2012-01-01

    Alexia may be divided into different subtypes, with semantic paralexia being particularly rare. A 57 year old woman with a discreet left thalamic stroke and semantic paralexia is described. Language evalution with the Boston Diagnostic Aphasia Battery confirmed the semantic paralexia (deep alexia). Multimodality magnetic resonance imaging brain scanning excluded other cerebral lesions. A good recovery ensued. PMID:22593810

  5. The Software Therapist: Usability Problem Diagnosis Through Latent Semantic Analysis

    DTIC Science & Technology

    2006-06-14

    engineering because of information losses. The causes of these losses can be attributed to a lack of an adequate conceptual framework for organizing...effective inputs to redesign for fixing the problems found during testing. These problems can be exacerbated by inadequate levels of training or...discovery of appropriate and effective solutions to these problems. For this project, the UAF was validated, extended with new material, and embodied in

  6. Semantically Interoperable XML Data

    PubMed Central

    Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

    2013-01-01

    XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups. PMID:25298789

  7. Biomedical semantics in the Semantic Web.

    PubMed

    Splendiani, Andrea; Burger, Albert; Paschke, Adrian; Romano, Paolo; Marshall, M Scott

    2011-03-07

    The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.

  8. Biomedical semantics in the Semantic Web

    PubMed Central

    2011-01-01

    The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences? We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th. PMID:21388570

  9. Semantic contextual cuing and visual attention.

    PubMed

    Goujon, Annabelle; Didierjean, André; Marmèche, Evelyne

    2009-02-01

    Since M. M. Chun and Y. Jiang's (1998) original study, a large body of research based on the contextual cuing paradigm has shown that the visuocognitive system is capable of capturing certain regularities in the environment in an implicit way. The present study investigated whether regularities based on the semantic category membership of the context can be learned implicitly and whether that learning depends on attention. The contextual cuing paradigm was used with lexical displays in which the semantic category of the contextual words either did or did not predict the target location. Experiments 1 and 2 revealed that implicit contextual cuing effects can be extended to semantic category regularities. Experiments 3 and 4 indicated an implicit contextual cuing effect when the predictive context appeared in an attended color but not when the predictive context appeared in an ignored color. However, when the previously ignored context suddenly became attended, it immediately facilitated performance. In contrast, when the previously attended context suddenly became ignored, no benefit was observed. Results suggest that the expression of implicit semantic knowledge depends on attention but that latent learning can nevertheless take place outside the attentional field.

  10. The computational complexity of alternative updating approaches for an SVD-encoded indexing scheme

    SciTech Connect

    Berry, M.W.; O`Brien, G.W.; Dumais, S.T.

    1995-12-01

    Latent Semantic Indexing (LSI) is a conceptual indexing technique which uses the truncated SVD to estimate the underlying latent semantic structure of word to document association. By computing a lower-rank approximation to the original term-document matrix, LSI dampens the effects of word choice variability by representing terms and documents using (orthogonal) left and right singular vectors. Current methods for adding new documents to an LSI database (folding-in documents) can have deteriorating effects on the orthogonality of the vectors used to represent documents in high-dimensional subspaces. An alternative approach which updates the original truncated SVD so as to preserve the orthogonality among document vectors corresponding to the new term-document matrix is presented. The cost of the numerical computations and available memory needed to update the SVD versus the potential inaccuracy of former updating methods presents an interesting tradeoff for LSI database management. The computational cost of recomputing the truncated SVD of perturbed term-document matrices, updating current truncated SVD`s of term-document matrices, and the folding-in of new documents into an existing LSI model is presented.

  11. Potentiation of latent inhibition.

    PubMed

    Rodriguez, Gabriel; Hall, Geoffrey

    2008-07-01

    Rats were given exposure either to an odor (almond) or a compound of odor plus taste (almond plus saline), prior to training in which the odor served as the conditioned stimulus. It was found, for both appetitive and aversive procedures, that conditioning was retarded by preexposure (a latent inhibition effect), and the extent of the retardation was greater in rats preexposed to the compound (i.e., latent inhibition to the odor was potentiated by the presence of the taste). In contrast, the presence of the taste during conditioning itself overshadowed learning about the odor. We argue that the presence of the salient taste in compound with the odor enhances the rate of associative learning, producing a rapid loss in the associability of the odor. This loss of associability will generate both overshadowing and the potentiation of latent inhibition that is observed after preexposure to the compound.

  12. Somatotopic Semantic Priming and Prediction in the Motor System

    PubMed Central

    Grisoni, Luigi; Dreyer, Felix R.; Pulvermüller, Friedemann

    2016-01-01

    The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., “kiss” in footstep sound context; “kick” in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound–word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain. PMID:26908635

  13. Somatotopic Semantic Priming and Prediction in the Motor System.

    PubMed

    Grisoni, Luigi; Dreyer, Felix R; Pulvermüller, Friedemann

    2016-05-01

    The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., "kiss" in footstep sound context; "kick" in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound-word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain.

  14. 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…

  15. 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…

  16. Is There Semantic Interference in Delayed Naming?

    ERIC Educational Resources Information Center

    Madebach, Andreas; Oppermann, Frank; Hantsch, Ansgar; Curda, Christian; Jescheniak, Jorg D.

    2011-01-01

    The semantic interference effect in the picture-word interference task is interpreted as an index of lexical competition in prominent speech production models. Janssen, Schirm, Mahon, and Caramazza (2008) challenged this interpretation on the basis of experiments with a novel version of this task, which introduced a task-switching component.…

  17. Measuring Latent Quantities

    ERIC Educational Resources Information Center

    McDonald, Roderick P.

    2011-01-01

    A distinction is proposed between measures and predictors of latent variables. The discussion addresses the consequences of the distinction for the true-score model, the linear factor model, Structural Equation Models, longitudinal and multilevel models, and item-response models. A distribution-free treatment of calibration and…

  18. Exploring MEDLINE Space with Random Indexing and Pathfinder Networks

    PubMed Central

    Cohen, Trevor

    2008-01-01

    The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search. PMID:18999236

  19. Exploring MEDLINE space with random indexing and pathfinder networks.

    PubMed

    Cohen, Trevor

    2008-11-06

    The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search.

  20. Semantic prosody and judgment.

    PubMed

    Hauser, David J; Schwarz, Norbert

    2016-07-01

    Some words tend to co-occur exclusively with a positive or negative context in natural language use, even though such valence patterns are not dictated by definitions or are part of the words' core meaning. These words contain semantic prosody, a subtle valenced meaning derived from co-occurrence in language. As language and thought are heavily intertwined, we hypothesized that semantic prosody can affect evaluative inferences about related ambiguous concepts. Participants inferred that an ambiguous medical outcome was more negative when it was caused, a verb with negative semantic prosody, than when it was produced, a synonymous verb with no semantic prosody (Studies 1a, 1b). Participants completed sentence fragments in a manner consistent with semantic prosody (Study 2), and semantic prosody affected various other judgments in line with evaluative inferences (estimates of an event's likelihood in Study 3). Finally, semantic prosody elicited both positive and negative evaluations of outcomes across a large set of semantically prosodic verbs (Study 4). Thus, semantic prosody can exert a strong influence on evaluative judgment. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  1. Semantic search via concept annealing

    NASA Astrophysics Data System (ADS)

    Dunkelberger, Kirk A.

    2007-04-01

    Annealing, in metallurgy and materials science, is a heat treatment wherein the microstructure of a material is altered, causing changes in its properties such as strength and hardness. We define concept annealing as a lexical, syntactic, and semantic expansion capability (the removal of defects and the internal stresses that cause term- and phrase-based search failure) coupled with a directed contraction capability (semantically-related terms, queries, and concepts nucleate and grow to replace those originally deformed by internal stresses). These two capabilities are tied together in a control loop mediated by the information retrieval precision and recall metrics coupled with intuition provided by the operator. The specific representations developed have been targeted at facilitating highly efficient and effective semantic indexing and searching. This new generation of Find capability enables additional processing (i.e. all-source tracking, relationship extraction, and total system resource management) at rates, precisions, and accuracies previously considered infeasible. In a recent experiment, an order magnitude reduction in time to actionable intelligence and nearly three orderss magnitude reduction in false alarm rate was achieved.

  2. Semantic significance: a new measure of feature salience.

    PubMed

    Montefinese, Maria; Ambrosini, Ettore; Fairfield, Beth; Mammarella, Nicola

    2014-04-01

    According to the feature-based model of semantic memory, concepts are described by a set of semantic features that contribute, with different weights, to the meaning of a concept. Interestingly, this theoretical framework has introduced numerous dimensions to describe semantic features. Recently, we proposed a new parameter to measure the importance of a semantic feature for the conceptual representation-that is, semantic significance. Here, with speeded verification tasks, we tested the predictive value of our index and investigated the relative roles of conceptual and featural dimensions on the participants' performance. The results showed that semantic significance is a good predictor of participants' verification latencies and suggested that it efficiently captures the salience of a feature for the computation of the meaning of a given concept. Therefore, we suggest that semantic significance can be considered an effective index of the importance of a feature in a given conceptual representation. Moreover, we propose that it may have straightforward implications for feature-based models of semantic memory, as an important additional factor for understanding conceptual representation.

  3. Linguistic Semantics: An Introduction.

    ERIC Educational Resources Information Center

    Lyons, John

    The book, designed as a textbook for introductory study of semantics within college-level linguistics, focuses on the study of meaning as it is systematically encoded in the vocabulary and grammar of natural languages. The term "semantics" is presumed here to include pragmatics. An introductory section explains fundamental theoretical and…

  4. The Semantic Learning Organization

    ERIC Educational Resources Information Center

    Sicilia, Miguel-Angel; Lytras, Miltiadis D.

    2005-01-01

    Purpose: The aim of this paper is introducing the concept of a "semantic learning organization" (SLO) as an extension of the concept of "learning organization" in the technological domain. Design/methodology/approach: The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop…

  5. Communication: General Semantics Perspectives.

    ERIC Educational Resources Information Center

    Thayer, Lee, Ed.

    This book contains the edited papers from the eleventh International Conference on General Semantics, titled "A Search for Relevance." The conference questioned, as a central theme, the relevance of general semantics in a world of wars and human misery. Reacting to a fundamental Korzybski-ian principle that man's view of reality is…

  6. The Semantic Learning Organization

    ERIC Educational Resources Information Center

    Sicilia, Miguel-Angel; Lytras, Miltiadis D.

    2005-01-01

    Purpose: The aim of this paper is introducing the concept of a "semantic learning organization" (SLO) as an extension of the concept of "learning organization" in the technological domain. Design/methodology/approach: The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop…

  7. Aging and Semantic Activation.

    ERIC Educational Resources Information Center

    Howard, Darlene V.

    Three studies tested the theory that long term memory consists of a semantically organized network of concept nodes interconnected by leveled associations or relations, and that when a stimulus is processed, the corresponding concept node is assumed to be temporarily activated and this activation spreads to nearby semantically related nodes. In…

  8. Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words.

    PubMed

    Hoffman, Paul; Lambon Ralph, Matthew A; Rogers, Timothy T

    2013-09-01

    Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

  9. Latent effects decision analysis

    DOEpatents

    Cooper, J Arlin [Albuquerque, NM; Werner, Paul W [Albuquerque, NM

    2004-08-24

    Latent effects on a system are broken down into components ranging from those far removed in time from the system under study (latent) to those which closely effect changes in the system. Each component is provided with weighted inputs either by a user or from outputs of other components. A non-linear mathematical process known as `soft aggregation` is performed on the inputs to each component to provide information relating to the component. This information is combined in decreasing order of latency to the system to provide a quantifiable measure of an attribute of a system (e.g., safety) or to test hypotheses (e.g., for forensic deduction or decisions about various system design options).

  10. Semantic Sensor Web

    NASA Astrophysics Data System (ADS)

    Sheth, A.; Henson, C.; Thirunarayan, K.

    2008-12-01

    Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the Semantic Sensor Web (SSW) [1] proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge. Kno.e.sis Center's approach to SSW is an evolutionary one. It adds semantic annotations to the existing standard sensor languages of the Sensor Web Enablement (SWE) defined by OGC. These annotations enhance primarily syntactic XML-based descriptions in OGC's SWE languages with microformats, and W3C's Semantic Web languages- RDF and OWL. In association with semantic annotation and semantic web capabilities including ontologies and rules, SSW supports interoperability, analysis and reasoning over heterogeneous multi-modal sensor data. In this presentation, we will also demonstrate a mashup with support for complex spatio-temporal-thematic queries [2] and semantic analysis that utilize semantic annotations, multiple ontologies and rules. It uses existing services (e.g., GoogleMap) and semantics enhanced SWE's Sensor Observation Service (SOS) over weather and road condition data from various sensors that are part of Ohio's transportation network. Our upcoming plans are to demonstrate end to end (heterogeneous sensor to application) semantics support and study scalability of SSW involving thousands of sensors to about a billion triples. Keywords: Semantic Sensor Web, Spatiotemporal thematic queries, Semantic Web Enablement, Sensor Observation Service [1] Amit Sheth, Cory Henson, Satya

  11. Retrieval of abstract semantics.

    PubMed

    Noppeney, Uta; Price, Cathy J

    2004-05-01

    Behavioural and neuropsychological evidence suggests that abstract and concrete concepts might be represented, retrieved and processed differently in the human brain. Using fMRI, we demonstrate that retrieval of abstract relative to sensory-based semantics during synonym judgements increased activation in a left frontotemporal system that has been associated with semantic processing particularly at the sentence level. Since activation increases were observed irrespective of the degree of difficulty, we suggest that these differential activations might reflect a particular retrieval mechanism or strategy for abstract concepts. In contrast to sensory-based semantics, the meaning of abstract concepts is largely specified by their usage in language rather than by their relations to the physical world. Subjects might therefore generate an appropriate semantic sentential context to fully explore and specify the meaning of abstract concepts. Our results also explain why abstract semantics is vulnerable to left frontotemporal lesions.

  12. Order Theoretical Semantic Recommendation

    SciTech Connect

    Joslyn, Cliff A.; Hogan, Emilie A.; Paulson, Patrick R.; Peterson, Elena S.; Stephan, Eric G.; Thomas, Dennis G.

    2013-07-23

    Mathematical concepts of order and ordering relations play multiple roles in semantic technologies. Discrete totally ordered data characterize both input streams and top-k rank-ordered recommendations and query output, while temporal attributes establish numerical total orders, either over time points or in the more complex case of startend temporal intervals. But also of note are the fully partially ordered data, including both lattices and non-lattices, which actually dominate the semantic strcuture of ontological systems. Scalar semantic similarities over partially-ordered semantic data are traditionally used to return rank-ordered recommendations, but these require complementation with true metrics available over partially ordered sets. In this paper we report on our work in the foundations of partial order measurement in ontologies, with application to top-k semantic recommendation in workflows.

  13. Enhancing medical database semantics.

    PubMed Central

    Leão, B. de F.; Pavan, A.

    1995-01-01

    Medical Databases deal with dynamic, heterogeneous and fuzzy data. The modeling of such complex domain demands powerful semantic data modeling methodologies. This paper describes GSM-Explorer a Case Tool that allows for the creation of relational databases using semantic data modeling techniques. GSM Explorer fully incorporates the Generic Semantic Data Model-GSM enabling knowledge engineers to model the application domain with the abstraction mechanisms of generalization/specialization, association and aggregation. The tool generates a structure that implements persistent database-objects through the automatic generation of customized SQL ANSI scripts that sustain the semantics defined in the higher lever. This paper emphasizes the system architecture and the mapping of the semantic model into relational tables. The present status of the project and its further developments are discussed in the Conclusions. PMID:8563288

  14. Semantics, Pragmatics, and the Nature of Semantic Theories

    ERIC Educational Resources Information Center

    Spewak, David Charles, Jr.

    2013-01-01

    The primary concern of this dissertation is determining the distinction between semantics and pragmatics and how context sensitivity should be accommodated within a semantic theory. I approach the question over how to distinguish semantics from pragmatics from a new angle by investigating what the objects of a semantic theory are, namely…

  15. Semantics, Pragmatics, and the Nature of Semantic Theories

    ERIC Educational Resources Information Center

    Spewak, David Charles, Jr.

    2013-01-01

    The primary concern of this dissertation is determining the distinction between semantics and pragmatics and how context sensitivity should be accommodated within a semantic theory. I approach the question over how to distinguish semantics from pragmatics from a new angle by investigating what the objects of a semantic theory are, namely…

  16. Latent common genetic components of obesity traits

    PubMed Central

    Harders, R; Luke, A; Zhu, X; Cooper, RS

    2008-01-01

    Background Obesity is rapidly becoming a global epidemic. Unlike many complex human diseases, obesity is defined not just by a single trait or phenotype, but jointly by measures of anthropometry and metabolic status. Methods We applied maximum likelihood factor analysis to identify common latent factors underlying observed covariance in multiple obesity-related measures. Both the genetic components and the mode of inheritance of the common factors were evaluated. A total of 1775 participants from 590 families for whom measures on obesity-related traits were available were included in this study. Results The average age of participants was 37 years, 39% of the participants were obese (body mass index ≥ 30.0 kg/m2) and 26% were overweight (body mass index 25.0 - 29.9 kg/m2). Two latent common factors jointly accounting for over 99% of the correlations among obesity-related traits were identified. Complex segregation analysis of the age and sex-adjusted latent factors provide evidence for a Mendelian mode of inheritance of major genetic effect with heritability estimates of 40.4% and 47.5% for the first and second factors, respectively. Conclusions These findings provide a support for multivariate-based approach for investigating pleiotropic effects on obesity-related traits which can be applied in both genetic linkage and association mapping. PMID:18936762

  17. An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

    NASA Astrophysics Data System (ADS)

    Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

    2014-02-01

    In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates

  18. Peach latent mosaic viroid: not so latent.

    PubMed

    Flores, Ricardo; Delgado, Sonia; Rodio, María-Elena; Ambrós, Silvia; Hernández, Carmen; Serio, Francesco D I

    2006-07-01

    SUMMARY Taxonomy: Peach latent mosaic viroid (PLMVd) is the type species of the genus Pelamoviroid within the family Avsunviroidae of chloroplastic viroids with hammerhead ribozymes. Physical properties: A small circular RNA of 336-351 nt (differences in size result from the absence or presence of certain insertions) adopting a branched conformation stabilized by a pseudoknot between two kissing loops. This particular conformation is most likely responsible for the insolubility of PLMVd in highly saline conditions (in which other viroids adopting a rod-like conformation are soluble). Both polarity strands are able to form hammerhead structures and to self-cleave during replication as predicted by these ribozymes. Biological properties: Although most infections occur without conspicuous symptoms, certain PLMVd isolates induce leaf mosaics, blotches and in the most extreme cases albinism (peach calico, PC), flower streaking, delays in foliation, flowering and ripening, deformations and decolorations of fruits, which usually present cracked sutures and enlarged roundish stones, bud necrosis, stem pitting and premature ageing of the trees, which also adopt a characteristic growing pattern (open habit). The molecular determinant for PC has been mapped at a 12-14-nt insertion that folds into a hairpin capped by a U-rich loop present only in certain variants. PLMVd is horizontally transmitted by the propagation of infected buds and to a lesser extent by pruning tools and aphids, but not by pollen; the viroid is not vertically transmitted through seed. Interesting features: This provides a suitable system for studying how a minimal non-protein-coding catalytic RNA replicates (subverting a DNA-dependent RNA polymerase to transcribe an RNA template), moves, interferes with the metabolism of its host (inciting specific symptoms and a defensive RNA silencing response) and evolves following a quasi-species model characterized by a complex spectrum of variants.

  19. CINDI: A Virtual Library Indexing and Discovery System.

    ERIC Educational Resources Information Center

    Desai, Bipin C.; Shinghal, Raijan; Shayan, Nader R.; Zhou, Youquan

    1999-01-01

    Describes a system called CINDI (Concordia INdexing and DIscovery System) for cataloging and searching documents in a distributed virtual library. The document author registers metadata in the form of a semantic header that contains information on syntactic and semantic content, and an expert system fills the semantic header according to accepted…

  20. A Defense of Semantic Minimalism

    ERIC Educational Resources Information Center

    Kim, Su

    2012-01-01

    Semantic Minimalism is a position about the semantic content of declarative sentences, i.e., the content that is determined entirely by syntax. It is defined by the following two points: "Point 1": The semantic content is a complete/truth-conditional proposition. "Point 2": The semantic content is useful to a theory of…

  1. A Semantic Graph Query Language

    SciTech Connect

    Kaplan, I L

    2006-10-16

    Semantic graphs can be used to organize large amounts of information from a number of sources into one unified structure. A semantic query language provides a foundation for extracting information from the semantic graph. The graph query language described here provides a simple, powerful method for querying semantic graphs.

  2. A Defense of Semantic Minimalism

    ERIC Educational Resources Information Center

    Kim, Su

    2012-01-01

    Semantic Minimalism is a position about the semantic content of declarative sentences, i.e., the content that is determined entirely by syntax. It is defined by the following two points: "Point 1": The semantic content is a complete/truth-conditional proposition. "Point 2": The semantic content is useful to a theory of…

  3. A predictive framework for evaluating models of semantic organization in free recall

    PubMed Central

    Morton, Neal W; Polyn, Sean M.

    2016-01-01

    Research in free recall has demonstrated that semantic associations reliably influence the organization of search through episodic memory. However, the specific structure of these associations and the mechanisms by which they influence memory search remain unclear. We introduce a likelihood-based model-comparison technique, which embeds a model of semantic structure within the context maintenance and retrieval (CMR) model of human memory search. Within this framework, model variants are evaluated in terms of their ability to predict the specific sequence in which items are recalled. We compare three models of semantic structure, latent semantic analysis (LSA), global vectors (GloVe), and word association spaces (WAS), and find that models using WAS have the greatest predictive power. Furthermore, we find evidence that semantic and temporal organization is driven by distinct item and context cues, rather than a single context cue. This finding provides important constraint for theories of memory search.

  4. Predicting protein-protein relationships from literature using latent topics.

    PubMed

    Aso, Tatsuya; Eguchi, Koji

    2009-10-01

    This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it has not been investigated for such a task. In this paper, we apply the state-of-the-art Collapsed Variational Bayesian Inference and Gibbs Sampling inference to estimating the LDA model. We also apply probabilistic Latent Semantic Analysis (pLSA) as a baseline for comparison, and compare them from the viewpoints of log-likelihood, classification accuracy and retrieval effectiveness. We demonstrate through experiments that the Collapsed Variational LDA gives better results than the others, especially in terms of classification accuracy and retrieval effectiveness in the task of the protein-protein relationship prediction.

  5. Identification of novel type III effectors using latent Dirichlet allocation.

    PubMed

    Yang, Yang

    2012-01-01

    Among the six secretion systems identified in Gram-negative bacteria, the type III secretion system (T3SS) plays important roles in the disease development of pathogens. T3SS has attracted a great deal of research interests. However, the secretion mechanism has not been fully understood yet. Especially, the identification of effectors (secreted proteins) is an important and challenging task. This paper adopts machine learning methods to identify type III secreted effectors (T3SEs). We extract features from amino acid sequences and conduct feature reduction based on latent semantic information by using latent Dirichlet allocation model. The experimental results on Pseudomonas syringae data set demonstrate the good performance of the new methods.

  6. Semantic Services for Wikipedia

    NASA Astrophysics Data System (ADS)

    Wang, Haofen; Penin, Thomas; Fu, Linyun; Liu, Qiaoling; Xue, Guirong; Yu, Yong

    Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It features many attractive characteristics, like entity-based link graph, abundant categorization and semi-structured layout, and can serve as an ideal data source to extract high quality and well-structured data. In this chapter, we first propose several solutions to extract knowledge from Wikipedia. We do not only consider information from the relational summaries of articles (infoboxes) but also semi-automatically extract it from the article text using the structured content available. Due to differences with information extraction from the Web, it is necessary to tackle new problems, like the lack of redundancy in Wikipedia that is dealt with by extending traditional machine learning algorithms to work with few labeled data. Furthermore, we also exploit the widespread categories as a complementary way to discover additional knowledge. Benefiting from both structured and textural information, we additionally provide a suggestion service for Wikipedia authoring. With the aim to facilitate semantic reuse, our proposal provides users with facilities such as link, categories and infobox content suggestions. The proposed enhancements can be applied to attract more contributors and lighten the burden of professional editors. Finally, we developed an enhanced search system, which can ease the process of exploiting Wikipedia. To provide a user-friendly interface, it extends the faceted search interface with relation navigation and let the user easily express his complex information needs in an interactive way. In order to achieve efficient query answering, it extends scalable IR engines to index and search both the textual and structured information with an integrated ranking support.

  7. Trusting Crowdsourced Geospatial Semantics

    NASA Astrophysics Data System (ADS)

    Goodhue, P.; McNair, H.; Reitsma, F.

    2015-08-01

    The degree of trust one can place in information is one of the foremost limitations of crowdsourced geospatial information. As with the development of web technologies, the increased prevalence of semantics associated with geospatial information has increased accessibility and functionality. Semantics also provides an opportunity to extend indicators of trust for crowdsourced geospatial information that have largely focused on spatio-temporal and social aspects of that information. Comparing a feature's intrinsic and extrinsic properties to associated ontologies provides a means of semantically assessing the trustworthiness of crowdsourced geospatial information. The application of this approach to unconstrained semantic submissions then allows for a detailed assessment of the trust of these features whilst maintaining the descriptive thoroughness this mode of information submission affords. The resulting trust rating then becomes an attribute of the feature, providing not only an indication as to the trustworthiness of a specific feature but is able to be aggregated across multiple features to illustrate the overall trustworthiness of a dataset.

  8. Algebraic Semantics for Narrative

    ERIC Educational Resources Information Center

    Kahn, E.

    1974-01-01

    This paper uses discussion of Edmund Spenser's "The Faerie Queene" to present a theoretical framework for explaining the semantics of narrative discourse. The algebraic theory of finite automata is used. (CK)

  9. Toward Automatic Determination of the Semantics of Connectives in Large Newspaper Corpora

    ERIC Educational Resources Information Center

    Bestgen, Yves; Degand, Liesbeth; Spooren, Wilbert

    2006-01-01

    We explored the possibility of using automatic techniques to analyze the use of backward causal connectives in large Dutch newspaper corpora. With the help of 2 techniques, Latent Semantic Analysis and Thematic Text Analysis, the contexts of more than 14,000 connectives were studied. The method of analysis is described. We found that differences…

  10. Relationships in Analogy Items: A Semantic Component of a Psychometric Task

    ERIC Educational Resources Information Center

    Whitely, Susan E.

    1977-01-01

    The verbal analogy item as a measure of intelligence is investigated. Using latent partition analysis, this study attempts to identify a semantic structure of relationships that individuals use to comprehend completed analogies. The implications for test construction and test validity are discussed. (Author/JKS)

  11. Music, language and meaning: brain signatures of semantic processing.

    PubMed

    Koelsch, Stefan; Kasper, Elisabeth; Sammler, Daniela; Schulze, Katrin; Gunter, Thomas; Friederici, Angela D

    2004-03-01

    Semantics is a key feature of language, but whether or not music can activate brain mechanisms related to the processing of semantic meaning is not known. We compared processing of semantic meaning in language and music, investigating the semantic priming effect as indexed by behavioral measures and by the N400 component of the event-related brain potential (ERP) measured by electroencephalography (EEG). Human subjects were presented visually with target words after hearing either a spoken sentence or a musical excerpt. Target words that were semantically unrelated to prime sentences elicited a larger N400 than did target words that were preceded by semantically related sentences. In addition, target words that were preceded by semantically unrelated musical primes showed a similar N400 effect, as compared to target words preceded by related musical primes. The N400 priming effect did not differ between language and music with respect to time course, strength or neural generators. Our results indicate that both music and language can prime the meaning of a word, and that music can, as language, determine physiological indices of semantic processing.

  12. The role of textual semantic constraints in knowledge-based inference generation during reading comprehension: A computational approach.

    PubMed

    Yeari, Menahem; van den Broek, Paul

    2015-01-01

    The present research adopted a computational approach to explore the extent to which the semantic content of texts constrains the activation of knowledge-based inferences. Specifically, we examined whether textual semantic constraints (TSC) can explain (1) the activation of predictive inferences, (2) the activation of bridging inferences and (3) the higher prevalence of the activation of bridging inferences compared to predictive inferences. To examine these hypotheses, we computed the strength of semantic associations between texts and probe items as presented to human readers in previous behavioural studies, using the Latent Semantic Analysis (LSA) algorithm. We tested whether stronger semantic associations are observed for inferred items compared to control items. Our results show that in 15 out of 17 planned comparisons, the computed strength of semantic associations successfully simulated the activation of inferences. These findings suggest that TSC play a central role in the activation of knowledge-based inferences.

  13. A Latent Transition Model with Logistic Regression

    ERIC Educational Resources Information Center

    Chung, Hwan; Walls, Theodore A.; Park, Yousung

    2007-01-01

    Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian…

  14. A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

    ERIC Educational Resources Information Center

    Leite, Walter L.

    2007-01-01

    Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…

  15. A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

    ERIC Educational Resources Information Center

    Leite, Walter L.

    2007-01-01

    Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…

  16. Reproducibility and discriminability of brain patterns of semantic categories enhanced by congruent audiovisual stimuli.

    PubMed

    Li, Yuanqing; Wang, Guangyi; Long, Jinyi; Yu, Zhuliang; Huang, Biao; Li, Xiaojian; Yu, Tianyou; Liang, Changhong; Li, Zheng; Sun, Pei

    2011-01-01

    One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern search method to extract brain patterns corresponding to two semantic categories: "old people" and "young people." These brain patterns were elicited by semantically congruent audiovisual, semantically incongruent audiovisual, unimodal visual, and unimodal auditory stimuli belonging to the two semantic categories. We calculated the reproducibility index, which measures the similarity of the patterns within the same category. We also decoded the semantic categories from these brain patterns. The decoding accuracy reflects the discriminability of the brain patterns between two categories. The results showed that both the reproducibility index of brain patterns and the decoding accuracy were significantly higher for semantically congruent audiovisual stimuli than for unimodal visual and unimodal auditory stimuli, while the semantically incongruent stimuli did not elicit brain patterns with significantly higher reproducibility index or decoding accuracy. Thus, the semantically congruent audiovisual stimuli enhanced the within-class reproducibility of brain patterns and the between-class discriminability of brain patterns, and facilitate neural representations of semantic categories or concepts. Furthermore, we analyzed the brain activity in superior temporal sulcus and middle temporal gyrus (STS/MTG). The strength of the fMRI signal and the reproducibility index were enhanced by the semantically congruent audiovisual stimuli. Our results support the use of the reproducibility index as a potential tool to supplement the fMRI signal amplitude for evaluating multimodal integration.

  17. Non-semantic contributions to "semantic" redundancy gain.

    PubMed

    Shepherdson, Peter; Miller, Jeff

    2016-01-01

    Recently, two groups of researchers have reported redundancy gains (enhanced performance with multiple, redundant targets) in tasks requiring semantic categorization. Here we report two experiments aimed at determining whether the gains found by one of these groups resulted from some form of semantic coactivation. We asked undergraduate psychology students to complete choice RT tasks requiring the semantic categorization of visually presented words, and compared performance with redundant targets from the same semantic category to performance with redundant targets from different semantic categories. If the redundancy gains resulted from the combination of information at a semantic level, they should have been greater in the former than the latter situation. However, our results showed no significant differences in redundancy gain (for latency and accuracy) between same-category and different-category conditions, despite gains appearing in both conditions. Thus, we suggest that redundancy gain in the semantic categorization task may result entirely from statistical facilitation or combination of information at non-semantic levels.

  18. Toward a brain-based componential semantic representation.

    PubMed

    Binder, Jeffrey R; Conant, Lisa L; Humphries, Colin J; Fernandino, Leonardo; Simons, Stephen B; Aguilar, Mario; Desai, Rutvik H

    2016-01-01

    Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and

  19. Effects of speed of word processing on semantic access: the case of bilingualism.

    PubMed

    Martin, Clara D; Costa, Albert; Dering, Benjamin; Hoshino, Noriko; Wu, Yan Jing; Thierry, Guillaume

    2012-01-01

    Bilingual speakers generally manifest slower word recognition than monolinguals. We investigated the consequences of the word processing speed on semantic access in bilinguals. The paradigm involved a stream of English words and pseudowords presented in succession at a constant rate. English-Welsh bilinguals and English monolinguals were asked to count the number of letters in pseudowords and actively disregard words. They were not explicitly told that pairs of words in immediate succession were embedded and could either be semantically related or not. We expected that slower word processing in bilinguals would result in semantic access indexed by semantic priming. As expected, bilinguals showed significant semantic priming, indexed by an N400 modulation, whilst monolinguals did not. Moreover, bilinguals were slower in performing the task. The results suggest that bilinguals cannot discriminate between pseudowords and words without accessing semantic information whereas monolinguals can dismiss English words on the basis of subsemantic information.

  20. The Semantic SPASE

    NASA Astrophysics Data System (ADS)

    Hughes, S.; Crichton, D.; Thieman, J.; Ramirez, P.; King, T.; Weiss, M.

    2005-12-01

    The Semantic SPASE (Space Physics Archive Search and Extract) prototype demonstrates the use of semantic web technologies to capture, document, and manage the SPASE data model, support facet- and text-based search, and provide flexible and intuitive user interfaces. The SPASE data model, under development since late 2003 by a consortium of space physics domain experts, is intended to serve as the basis for interoperability between independent data systems. To develop the Semantic SPASE prototype, the data model was first analyzed to determine the inherit object classes and their attributes. These were entered into Stanford Medical Informatics' Protege ontology tool and annotated using definitions from the SPASE documentation. Further analysis of the data model resulted in the addition of class relationships. Finally attributes and relationships that support broad-scope interoperability were added from research associated with the Object-Oriented Data Technology task. To validate the ontology and produce a knowledge base, example data products were ingested. The capture of the data model as an ontology results in a more formal specification of the model. The Protege software is also a powerful management tool and supports plug-ins that produce several graphical notations as output. The stated purpose of the semantic web is to support machine understanding of web-based information. Protege provides an export capability to RDF/XML and RDFS/XML for this purpose. Several research efforts use RDF/XML knowledge bases to provide semantic search. MIT's Simile/Longwell project provides both facet- and text-based search using a suite of metadata browsers and the text-based search engine Lucene. Using the Protege generated RDF knowledge-base a semantic search application was easily built and deployed to run as a web application. Configuration files specify the object attributes and values to be designated as facets (i.e. search) constraints. Semantic web technologies provide

  1. Bridging the semantic gap in sports

    NASA Astrophysics Data System (ADS)

    Li, Baoxin; Errico, James; Pan, Hao; Sezan, M. Ibrahim

    2003-01-01

    One of the major challenges facing current media management systems and the related applications is the so-called "semantic gap" between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs. The framework is based on a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. An MPEG-7 compliant prototype browsing system has been implemented to demonstrate semantic retrieval and summarization of sports video.

  2. Temporal Representation in Semantic Graphs

    SciTech Connect

    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.

  3. Impact of Latent Infection Treatment in Indigenous Populations

    PubMed Central

    Yuhara, Lucia Suemi; Sacchi, Flávia Patussi Correia; Croda, Julio

    2013-01-01

    The aims of the present study were to identify risk factors associated with latent tuberculosis (TB), examine the development of active disease among contacts, and assess the effectiveness of treating latent infection in indigenous Brazilians from January 2006 to December 2011. This was a retrospective study consisting of 1,371 tuberculosis contacts, 392 of whom underwent treatment for latent infection. Morbidity-from-TB data were obtained from the Information System for Disease Notification (SINAN) database, and the contacts’ data were collected from the clinical records using forms employed by Special Department of Indigenous Health (SESAI) multidisciplinary teams, according to SESAI’s instructions. The variables that were associated with latent infection among the contacts were age (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 1.02–1.04) and close contact with a smear-positive index case (OR: 2.26, 95% CI: 1.59–3.22). The variables associated with the development of active TB among the contacts were a tuberculin skin test (TST) ≥10 mm (relative risk [RR]: 1.12, 95% CI: 1.07–1.17), age (RR: 1.01, 95% CI: 1.00–1.03), and treatment of latent infection (RR: 0.03, 95% CI: 0.01–0.27). The estimated number of latent infection treatments needed to prevent one case of active TB among the contacts was 51 treatments (95% CI: 33–182). In contacts with TST ≥10 mm, 10 (95% CI: 6–19) latent infection treatments were necessary to prevent one case of active TB. Age and close contact with a smear-positive index case were associated with latent TB. Screening with TST is a high priority among individuals contacting smear-positive index cases. Age and TST are associated with the development of active TB among contacts, and treatment of latent infection is an effective measure to control TB in indigenous communities. PMID:23936264

  4. Semantic Search of Web Services

    ERIC Educational Resources Information Center

    Hao, Ke

    2013-01-01

    This dissertation addresses semantic search of Web services using natural language processing. We first survey various existing approaches, focusing on the fact that the expensive costs of current semantic annotation frameworks result in limited use of semantic search for large scale applications. We then propose a vector space model based service…

  5. Semantic memory in object use.

    PubMed

    Silveri, Maria Caterina; Ciccarelli, Nicoletta

    2009-10-01

    We studied five patients with semantic memory disorders, four with semantic dementia and one with herpes simplex virus encephalitis, to investigate the involvement of semantic conceptual knowledge in object use. Comparisons between patients who had semantic deficits of different severity, as well as the follow-up, showed that the ability to use objects was largely preserved when the deficit was mild but progressively decayed as the deficit became more severe. Naming was generally more impaired than object use. Production tasks (pantomime execution and actual object use) and comprehension tasks (pantomime recognition and action recognition) as well as functional knowledge about objects were impaired when the semantic deficit was severe. Semantic and unrelated errors were produced during object use, but actions were always fluent and patients performed normally on a novel tools task in which the semantic demand was minimal. Patients with severe semantic deficits scored borderline on ideational apraxia tasks. Our data indicate that functional semantic knowledge is crucial for using objects in a conventional way and suggest that non-semantic factors, mainly non-declarative components of memory, might compensate to some extent for semantic disorders and guarantee some residual ability to use very common objects independently of semantic knowledge.

  6. Semantic Search of Web Services

    ERIC Educational Resources Information Center

    Hao, Ke

    2013-01-01

    This dissertation addresses semantic search of Web services using natural language processing. We first survey various existing approaches, focusing on the fact that the expensive costs of current semantic annotation frameworks result in limited use of semantic search for large scale applications. We then propose a vector space model based service…

  7. LATENT LIFE OF ARTERIES.

    PubMed

    Carrel, A

    1910-07-23

    When a segment of artery, killed by heat, formalin or glycerin is transplanted, it undergoes a rapid degeneration. Its muscle fibers disappear while the tissue of the host reacts by building a new wall of connective tissue. When the transplanted vessel has been preserved in a condition of latent life, no degeneration of the wall occurs, or the wall undergoes only partial degeneration. The muscle fibers can keep their normal appearance, even for a long time after the operation. It is, therefore, demonstrated that arteries can be preserved outside of the body in a condition of unmanifested actual life. The best method of preservation consists of placing the vessels, immersed in vaselin, in an ice box, the temperature of which is slightly above the freezing point. From a surgical standpoint, the transplantation of preserved vessels can be used with some safety. When the arteries were kept in defibrinated blood or vaselin and in cold storage, the proportion of positive results was 75 and 80 per cent., and this can probably be increased.

  8. Arabic Literature: Uniterm Indexing System for Storage and Retrieval.

    ERIC Educational Resources Information Center

    Ghani, Abdul

    1987-01-01

    Describes the uniterm system of coordinate indexing and examines its applicability to the indexing of Arabic literature. Unresolved problems in semantics, thesauri development, and standardization are identified and discussed. (CLB)

  9. Latent Dirichlet allocation models for image classification.

    PubMed

    Rasiwasia, Nikhil; Vasconcelos, Nuno

    2013-11-01

    Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and class-specific-simplex LDA (css-LDA), are proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification. To address this, ts-LDA models are introduced which replace the automated topic discovery of LDA with specified topics, identical to the classes of interest for classification. While this results in improvements in classification accuracy over existing LDA models, it compromises the ability of LDA to discover unanticipated structure of interest. This limitation is addressed by the introduction of css-LDA, an LDA model with class supervision at the level of image features. In css-LDA topics are discovered per class, i.e., a single set of topics shared across classes is replaced by multiple class-specific topic sets. The css-LDA model is shown to combine the labeling strength of topic-supervision with the flexibility of topic-discovery. Its effectiveness is demonstrated through an extensive experimental evaluation, involving multiple benchmark datasets, where it is shown to outperform existing LDA-based image classification approaches.

  10. Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation.

    PubMed

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

    Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as 'CHEMICAL-1 compared to CHEMICAL-2' With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical-disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked order

  11. Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation

    PubMed Central

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

    Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as ‘CHEMICAL-1 compared to CHEMICAL-2.’ With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical–disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked

  12. Semantator: semantic annotator for converting biomedical text to linked data.

    PubMed

    Tao, Cui; Song, Dezhao; Sharma, Deepak; Chute, Christopher G

    2013-10-01

    More than 80% of biomedical data is embedded in plain text. The unstructured nature of these text-based documents makes it challenging to easily browse and query the data of interest in them. One approach to facilitate browsing and querying biomedical text is to convert the plain text to a linked web of data, i.e., converting data originally in free text to structured formats with defined meta-level semantics. In this paper, we introduce Semantator (Semantic Annotator), a semantic-web-based environment for annotating data of interest in biomedical documents, browsing and querying the annotated data, and interactively refining annotation results if needed. Through Semantator, information of interest can be either annotated manually or semi-automatically using plug-in information extraction tools. The annotated results will be stored in RDF and can be queried using the SPARQL query language. In addition, semantic reasoners can be directly applied to the annotated data for consistency checking and knowledge inference. Semantator has been released online and was used by the biomedical ontology community who provided positive feedbacks. Our evaluation results indicated that (1) Semantator can perform the annotation functionalities as designed; (2) Semantator can be adopted in real applications in clinical and transactional research; and (3) the annotated results using Semantator can be easily used in Semantic-web-based reasoning tools for further inference.

  13. Understanding Latent Heat of Vaporization.

    ERIC Educational Resources Information Center

    Linz, Ed

    1995-01-01

    Presents a simple exercise for students to do in the kitchen at home to determine the latent heat of vaporization of water using typical household materials. Designed to stress understanding by sacrificing precision for simplicity. (JRH)

  14. Understanding Latent Heat of Vaporization.

    ERIC Educational Resources Information Center

    Linz, Ed

    1995-01-01

    Presents a simple exercise for students to do in the kitchen at home to determine the latent heat of vaporization of water using typical household materials. Designed to stress understanding by sacrificing precision for simplicity. (JRH)

  15. Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections.

    PubMed

    Cohen, Trevor; Schvaneveldt, Roger; Widdows, Dominic

    2010-04-01

    The discovery of implicit connections between terms that do not occur together in any scientific document underlies the model of literature-based knowledge discovery first proposed by Swanson. Corpus-derived statistical models of semantic distance such as Latent Semantic Analysis (LSA) have been evaluated previously as methods for the discovery of such implicit connections. However, LSA in particular is dependent on a computationally demanding method of dimension reduction as a means to obtain meaningful indirect inference, limiting its ability to scale to large text corpora. In this paper, we evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language. Proponents of this method have achieved comparable performance to LSA on several cognitive tasks while using a simpler and less computationally demanding method of dimension reduction than LSA employs. In this paper, we demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections, and evaluate Reflective Random Indexing (RRI), an iterative variant of the method that is better able to perform indirect inference. RRI is shown to lead to more clearly related indirect connections and to outperform existing RI implementations in the prediction of future direct co-occurrence in the MEDLINE corpus. 2009 Elsevier Inc. All rights reserved.

  16. Predicting Latent Class Scores for Subsequent Analysis

    ERIC Educational Resources Information Center

    Petersen, Janne; Bandeen-Roche, Karen; Budtz-Jorgensen, Esben; Larsen, Klaus Groes

    2012-01-01

    Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose…

  17. Predicting Latent Class Scores for Subsequent Analysis

    ERIC Educational Resources Information Center

    Petersen, Janne; Bandeen-Roche, Karen; Budtz-Jorgensen, Esben; Larsen, Klaus Groes

    2012-01-01

    Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose…

  18. Semantic Processing in Children and Adults: Incongruity and the N400

    ERIC Educational Resources Information Center

    Benau, Erik M.; Morris, Joanna; Couperus, J. W.

    2011-01-01

    Semantic processing in 10-year-old children and adults was examined using event related potentials (ERPs). The N400 component, an index of semantic processing, was studied in relation to sentences that ended with congruent, moderately incongruent, or strongly incongruent words. N400 amplitude in adults corresponded to levels of semantic…

  19. A Revised Semantic Differential Scale Distinguishing between Negative and Positive God Images

    ERIC Educational Resources Information Center

    Francis, Leslie J.; Robbins, Mandy; Gibson, Harry M.

    2006-01-01

    A sample of 755 school pupils between the ages of 11 and 18 years completed the Benson and Spilka semantic differential measure of God images. Factor analysis indicated the advantages of re-scoring the measure as an eight item unidimensional index, defining semantic space relating to God images ranging from negative affect to positive affect.…

  20. A Revised Semantic Differential Scale Distinguishing between Negative and Positive God Images

    ERIC Educational Resources Information Center

    Francis, Leslie J.; Robbins, Mandy; Gibson, Harry M.

    2006-01-01

    A sample of 755 school pupils between the ages of 11 and 18 years completed the Benson and Spilka semantic differential measure of God images. Factor analysis indicated the advantages of re-scoring the measure as an eight item unidimensional index, defining semantic space relating to God images ranging from negative affect to positive affect.…

  1. Quantitative Aspects of Single-Word Free Associations to Sentences Varying in Semantic Integration.

    ERIC Educational Resources Information Center

    Rosenberg, Sheldon

    It was anticipated that the single-word free association responses to sentences varying in degree of semantic integration (as indexed by sentence norms) would differ quantitatively. One group of 60 undergraduates was given a list of 16 sentences characterized by high semantic integration (HSI), while another group of 60 undergraduates received a…

  2. Semantic Processing in Children and Adults: Incongruity and the N400

    ERIC Educational Resources Information Center

    Benau, Erik M.; Morris, Joanna; Couperus, J. W.

    2011-01-01

    Semantic processing in 10-year-old children and adults was examined using event related potentials (ERPs). The N400 component, an index of semantic processing, was studied in relation to sentences that ended with congruent, moderately incongruent, or strongly incongruent words. N400 amplitude in adults corresponded to levels of semantic…

  3. Environmental Attitudes Semantic Differential.

    ERIC Educational Resources Information Center

    Mehne, Paul R.; Goulard, Cary J.

    This booklet is an evaluation instrument which utilizes semantic differential data to assess environmental attitudes. Twelve concepts are included: regulated access to beaches, urban planning, dune vegetation, wetlands, future cities, reclaiming wetlands for building development, city parks, commercial development of beaches, existing cities,…

  4. "Dyslexia": Toward Semantical Clarification.

    ERIC Educational Resources Information Center

    Manzo, Anthony V.; Duffelmeyer, Fred

    A formulated definition of the term dyslexia is proposed in this paper in order to clarify the semantical confusion which exists among both specialists and the general public. Dyslexia is explained as a generic term for severe and puzzling reading disability, found to be both acute (where reading-age lags 25 percent or more below mental age) and…

  5. Are Terminologies Semantically Uninteresting?

    ERIC Educational Resources Information Center

    Jacobson, Sven

    Some semanticists have argued that technical vocabulary or terminology is extralinguistic and therefore semantically uninteresting. However, no boundary exists in linguistic reality between terminology and ordinary vocabulary. Rather, terminologies and ordinary language exist on a continuum, and terminology is therefore a legitimate field for…

  6. Semantically Grounded Briefings

    DTIC Science & Technology

    2005-12-01

    occurring relations. AeroText and consequently AeroDAML can be tailored to particular domains through training sessions with annotated corpuses...the complexities of semantic markup by using mnemonic names for URIs, hiding unnamed intermediate objects (represented by “ GenSym ” identifiers), and

  7. Semantic physical science

    PubMed Central

    2012-01-01

    The articles in this special issue arise from a workshop and symposium held in January 2012 (Semantic Physical Science’). We invited people who shared our vision for the potential of the web to support chemical and related subjects. Other than the initial invitations, we have not exercised any control over the content of the contributed articles. PMID:22856527

  8. Semantic and Lexical Coherence.

    ERIC Educational Resources Information Center

    Fahnestock, Jeanne

    Helping students understand coherence in terms of the lexical ties and semantic relations possible between clauses and sentences formalizes an area of writing instruction that has been somewhat vague before and makes the process of creating a coherent paragraph less mysterious. Many students do not have the intuitive knowledge base for absorbing…

  9. Semantic Space Analyst

    SciTech Connect

    2004-04-15

    The Semantic Space Analyst (SSA) is software for analyzing a text corpus, discovering relationships among terms, and allowing the user to explore that information in different ways. It includes features for displaying and laying out terms and relationships visually, for generating such maps from manual queries, for discovering differences between corpora. Data can also be exported to Microsoft Excel.

  10. Semantic Web Development

    DTIC Science & Technology

    2006-09-01

    many documents are not expressible in logica at all, and many in logic but not in N3. However, we are building a system for which a prime goal is the...demonstrate that conventional logica programming tools are efficent and straightforwradly adapted to semantic web work. • Jena RDF toolkit now accepts N3 as

  11. GOOSE: semantic search on internet connected sensors

    NASA Astrophysics Data System (ADS)

    Schutte, Klamer; Bomhof, Freek; Burghouts, Gertjan; van Diggelen, Jurriaan; Hiemstra, Peter; van't Hof, Jaap; Kraaij, Wessel; Pasman, Huib; Smith, Arthur; Versloot, Corne; de Wit, Joost

    2013-05-01

    More and more sensors are getting Internet connected. Examples are cameras on cell phones, CCTV cameras for traffic control as well as dedicated security and defense sensor systems. Due to the steadily increasing data volume, human exploitation of all this sensor data is impossible for effective mission execution. Smart access to all sensor data acts as enabler for questions such as "Is there a person behind this building" or "Alert me when a vehicle approaches". The GOOSE concept has the ambition to provide the capability to search semantically for any relevant information within "all" (including imaging) sensor streams in the entire Internet of sensors. This is similar to the capability provided by presently available Internet search engines which enable the retrieval of information on "all" web pages on the Internet. In line with current Internet search engines any indexing services shall be utilized cross-domain. The two main challenge for GOOSE is the Semantic Gap and Scalability. The GOOSE architecture consists of five elements: (1) an online extraction of primitives on each sensor stream; (2) an indexing and search mechanism for these primitives; (3) a ontology based semantic matching module; (4) a top-down hypothesis verification mechanism and (5) a controlling man-machine interface. This paper reports on the initial GOOSE demonstrator, which consists of the MES multimedia analysis platform and the CORTEX action recognition module. It also provides an outlook into future GOOSE development.

  12. Comparing the performance of two CBIRS indexing schemes

    NASA Astrophysics Data System (ADS)

    Mueller, Wolfgang; Robbert, Guenter; Henrich, Andreas

    2003-01-01

    Content based image retrieval (CBIR) as it is known today has to deal with a number of challenges. Quickly summarized, the main challenges are firstly, to bridge the semantic gap between high-level concepts and low-level features using feedback, secondly to provide performance under adverse conditions. High-dimensional spaces, as well as a demanding machine learning task make the right way of indexing an important issue. When indexing multimedia data, most groups opt for extraction of high-dimensional feature vectors from the data, followed by dimensionality reduction like PCA (Principal Components Analysis) or LSI (Latent Semantic Indexing). The resulting vectors are indexed using spatial indexing structures such as kd-trees or R-trees, for example. Other projects, such as MARS and Viper propose the adaptation of text indexing techniques, notably the inverted file. Here, the Viper system is the most direct adaptation of text retrieval techniques to quantized vectors. However, while the Viper query engine provides decent performance together with impressive user-feedback behavior, as well as the possibility for easy integration of long-term learning algorithms, and support for potentially infinite feature vectors, there has been no comparison of vector-based methods and inverted-file-based methods under similar conditions. In this publication, we compare a CBIR query engine that uses inverted files (Bothrops, a rewrite of the Viper query engine based on a relational database), and a CBIR query engine based on LSD (Local Split Decision) trees for spatial indexing using the same feature sets. The Benchathlon initiative works on providing a set of images and ground truth for simulating image queries by example and corresponding user feedback. When performing the Benchathlon benchmark on a CBIR system (the System Under Test, SUT), a benchmarking harness connects over internet to the SUT, performing a number of queries using an agreed-upon protocol, the multimedia

  13. [PSA variations in persons with latent prostate cancer].

    PubMed

    Stamatiou, K; Danciu, M; Karakos, C; Sofras, F

    2008-01-01

    The introduction and common use of serum PSA (Prostate Specific Antigen) has been demonstrated a useful index on latent prostate cancer diagnostic but in the same time has increased surgical intervention on histological forms with no eventual future evolution. Benign comportment of latent carcinomas being well known in advance, we correlated in vitro serum PSA from latent tumors, with the samples from a control group (prostates without signs of malignization). Levels of PSA were slightly elevated compared to age norms, mainly in cases with a large coexistent hypertrophy. Our reduced sample does not stand any statistic analysis, but this observation could eventually explain increased diagnostic and hyper-treatment of non-important carcinomas from a clinical point of view.

  14. Semantic processing in children and adults: incongruity and the N400.

    PubMed

    Benau, Erik M; Morris, Joanna; Couperus, J W

    2011-06-01

    Semantic processing in 10-year-old children and adults was examined using event related potentials (ERPs). The N400 component, an index of semantic processing, was studied in relation to sentences that ended with congruent, moderately incongruent, or strongly incongruent words. N400 amplitude in adults corresponded to levels of semantic incongruity with the greatest amplitude occurring to strongly incongruent sentences at all midline electrodes. In contrast, children's N400s were greater for both moderately and strongly incongruent sentences but did not differ between these levels of incongruity. This finding suggests that semantic processing may differ in adults and children.

  15. Class-specific Gaussian-multinomial latent Dirichlet allocation for image annotation

    NASA Astrophysics Data System (ADS)

    Qian, Zhiming; Zhong, Ping; Wang, Runsheng

    2015-12-01

    Image annotation has been a challenging problem due to the well-known semantic gap between two heterogeneous information modalities, i.e., the visual modality referring to low-level visual features and the semantic modality referring to high-level human concepts. To bridge the semantic gap, we present an extension of latent Dirichlet allocation (LDA), denoted as class-specific Gaussian-multinomial latent Dirichlet allocation (csGM-LDA), in an effort to simulate the human's visual perception system. An analysis of previous supervised LDA models shows that the topics discovered by generative LDA models are driven by general image regularities rather than the semantic regularities for image annotation. To address this, csGM-LDA is introduced by using class supervision at the level of visual features for multimodal topic modeling. The csGM-LDA model combines the labeling strength of topic supervision with the flexibility of topic discovery, and the modeling problem can be effectively solved by a variational expectation-maximization (EM) algorithm. Moreover, as natural images usually generate an enormous size of high-dimensional data in annotation applications, an efficient descriptor based on Laplacian regularized uncorrelated tensor representation is proposed for explicitly exploiting the manifold structures in the high-order image space. Experimental results on two standard annotation datasets have shown the effectiveness of the proposed method by comparing with several state-of-the-art annotation methods.

  16. Epigenotypes of latent herpesvirus genomes.

    PubMed

    Minarovits, J

    2006-01-01

    Epigenotypes are modified cellular or viral genotypes which differ in transcriptional activity in spite of having an identical (or nearly identical) DNA sequence. Restricted expression of latent, episomal herpesvirus genomes is also due to epigenetic modifications. There is no virus production (lytic viral replication, associated with the expression of all viral genes) in tight latency. In vitro experiments demonstrated that DNA methylation could influence the activity of latent (and/or crucial lytic) promoters of prototype strains belonging to the three herpesvirus subfamilies (alpha-, beta-, and gamma-herpesviruses). In vivo, however, DNA methylation is not a major regulator of herpes simplex virus type 1 (HSV-1, a human alpha-herpesvirus) latent gene expression in neurons of infected mice. In these cells, the promoter/enhancer region of latency-associated transcripts (LATs) is enriched with acetyl histone H3, suggesting that histone modifications may control HSV-1 latency in terminally differentiated, quiescent neurons. Epstein-Barr virus (EBV, a human gamma-herpesvirus) is associated with a series of neoplasms. Latent, episomal EBV genomes are subject to host cell-dependent epigenetic modifications (DNA methylation, binding of proteins and protein complexes, histone modifications). The distinct viral epigenotypes are associated with distinct EBV latency types, i.e., cell type-specific usage of latent EBV promoters controlling the expression of latent, growth transformation-associated EBV genes. The contribution of major epigenetic mechanisms to the regulation of latent EBV promoters is variable. DNA methylation contributes to silencing of Wp and Cp (alternative promoters for transcripts coding for the nuclear antigens EBNA 1-6) and LMP1p, LMP2Ap, and LMP2Bp (promoters for transcripts encoding transmembrane proteins). DNA methylation does not control, however, Qp (a promoter for EBNA1 transcripts only) in lymphoblastoid cell lines (LCLs), although in vitro

  17. Exploring context and content links in social media: a latent space method.

    PubMed

    Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

    2012-05-01

    Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

  18. Effects of semantic predictability and regional dialect on vowel space reduction.

    PubMed

    Clopper, Cynthia G; Pierrehumbert, Janet B

    2008-09-01

    This study explored the interaction between semantic predictability and regional dialect variation in an analysis of speech produced by college-aged female talkers from the Northern, Midland, and Southern dialects of American English. Previous research on the effects of semantic predictability has shown that vowels in high semantic predictability contexts are temporally and spectrally reduced compared to vowels in low semantic predictability contexts. In the current study, an analysis of vowel duration confirmed temporal reduction in the high predictability condition. An analysis of vowel formant structure and vowel space dispersion revealed overall spectral reduction for the Southern talkers. For the Northern talkers, more extreme Northern Cities shifting occurred in the high predictability condition than in the low predictability condition. No effects of semantic predictability were observed for the Midland talkers. These findings suggest an interaction between semantic and indexical factors in vowel reduction processes.

  19. From Data to Semantic Information

    NASA Astrophysics Data System (ADS)

    Floridi, Luciano

    2003-06-01

    There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates the important implications of the revised definition for the analysis of the deflationary theories of truth, the standard definition of knowledge and the classic, quantitative theory of semantic information.

  20. Latent geometry of bipartite networks

    NASA Astrophysics Data System (ADS)

    Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

    2017-03-01

    Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

  1. Latent geometry of bipartite networks.

    PubMed

    Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

    2017-03-01

    Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

  2. The latent class twin method.

    PubMed

    Baker, Stuart G

    2016-09-01

    The twin method refers to the use of data from same-sex identical and fraternal twins to estimate the genetic and environmental contributions to a trait or outcome. The standard twin method is the variance component twin method that estimates heritability, the fraction of variance attributed to additive genetic inheritance. The latent class twin method estimates two quantities that are easier to interpret than heritability: the genetic prevalence, which is the fraction of persons in the genetic susceptibility latent class, and the heritability fraction, which is the fraction of persons in the genetic susceptibility latent class with the trait or outcome. We extend the latent class twin method in three important ways. First, we incorporate an additive genetic model to broaden the sensitivity analysis beyond the original autosomal dominant and recessive genetic models. Second, we specify a separate survival model to simplify computations and improve convergence. Third, we show how to easily adjust for covariates by extending the method of propensity scores from a treatment difference to zygosity. Applying the latent class twin method to data on breast cancer among Nordic twins, we estimated a genetic prevalence of 1%, a result with important implications for breast cancer prevention research. © 2016, The International Biometric Society.

  3. Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach.

    PubMed

    Proust-Lima, Cécile; Dartigues, Jean-François; Jacqmin-Gadda, Hélène

    2016-02-10

    Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually observed, and multiple longitudinal markers are collected when the true latent trait of interest is hard to capture (e.g., quality of life, functional dependency, and cognitive level). These multivariate and longitudinal data also usually have nonstandard distributions (discrete, asymmetric, bounded, etc.). We propose a joint model based on a latent process and latent classes to analyze simultaneously such multiple longitudinal markers of different natures, and multiple causes of progression. A latent process model describes the latent trait of interest and links it to the observed longitudinal outcomes using flexible measurement models adapted to different types of data, and a latent class structure links the longitudinal and cause-specific survival models. The joint model is estimated in the maximum likelihood framework. A score test is developed to evaluate the assumption of conditional independence of the longitudinal markers and each cause of progression given the latent classes. In addition, individual dynamic cumulative incidences of each cause of progression based on the repeated marker data are derived. The methodology is validated in a simulation study and applied on real data about cognitive aging obtained from a large population-based study. The aim is to predict the risk of dementia by accounting for the competing death according to the profiles of semantic memory measured by two asymmetric psychometric tests. Copyright © 2015 John Wiley & Sons, Ltd.

  4. SEMANTIC INFORMATION EXTRACTION FROM MULTISPECTRAL GEOSPATIAL IMAGERY VIA A FLEXIBLE FRAMEWORK

    SciTech Connect

    Gleason, Shaun Scott; Ferrell, Regina Kay; Cheriyadat, Anil M; Vatsavai, Raju; De, Soumya

    2010-01-01

    Identification and automatic labeling of facilities in high-resolution satellite images is a challenging task as the current thematic classification schemes and the low-level image features are not good enough to capture complex objects and their spatial relationships. In this paper we present a novel algorithm framework for automated semantic labeling of large image collections. The framework consists of various segmentation, feature extraction, vector quantization, and Latent Dirichlet Allocation modules. Initial experimental results show promise as well as the challenges in semantic classification technology development for nuclear proliferation monitoring.

  5. Subliminal Semantic Priming in Speech

    PubMed Central

    Tillmann, Barbara; Perrin, Fabien

    2011-01-01

    Numerous studies have reported subliminal repetition and semantic priming in the visual modality. We transferred this paradigm to the auditory modality. Prime awareness was manipulated by a reduction of sound intensity level. Uncategorized prime words (according to a post-test) were followed by semantically related, unrelated, or repeated target words (presented without intensity reduction) and participants performed a lexical decision task (LDT). Participants with slower reaction times in the LDT showed semantic priming (faster reaction times for semantically related compared to unrelated targets) and negative repetition priming (slower reaction times for repeated compared to semantically related targets). This is the first report of semantic priming in the auditory modality without conscious categorization of the prime. PMID:21655277

  6. Semantic annotation for biological information retrieval system.

    PubMed

    Oshaiba, Mohamed Marouf Z; El Houby, Enas M F; Salah, Akram

    2015-01-01

    Online literatures are increasing in a tremendous rate. Biological domain is one of the fast growing domains. Biological researchers face a problem finding what they are searching for effectively and efficiently. The aim of this research is to find documents that contain any combination of biological process and/or molecular function and/or cellular component. This research proposes a framework that helps researchers to retrieve meaningful documents related to their asserted terms based on gene ontology (GO). The system utilizes GO by semantically decomposing it into three subontologies (cellular component, biological process, and molecular function). Researcher has the flexibility to choose searching terms from any combination of the three subontologies. Document annotation is taking a place in this research to create an index of biological terms in documents to speed the searching process. Query expansion is used to infer semantically related terms to asserted terms. It increases the search meaningful results using the term synonyms and term relationships. The system uses a ranking method to order the retrieved documents based on the ranking weights. The proposed system achieves researchers' needs to find documents that fit the asserted terms semantically.

  7. Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

    PubMed Central

    Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

    2013-01-01

    This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model. PMID:23799009

  8. Effects of Latent Toxoplasmosis on Autoimmune Thyroid Diseases in Pregnancy

    PubMed Central

    Kaňková, Šárka; Procházková, Lucie; Flegr, Jaroslav; Calda, Pavel; Springer, Drahomíra; Potluková, Eliška

    2014-01-01

    Background Toxoplasmosis, one of the most common zoonotic diseases worldwide, can induce various hormonal and behavioural alterations in infected hosts, and its most common form, latent toxoplasmosis, influences the course of pregnancy. Autoimmune thyroid diseases (AITD) belong to the well-defined risk factors for adverse pregnancy outcomes. The aim of this study was to investigate whether there is a link between latent toxoplasmosis and maternal AITD in pregnancy. Methods Cross-sectional study in 1248 consecutive pregnant women in the 9–12th gestational weeks. Serum thyroid-stimulating hormone (TSH), thyroperoxidase antibodies (TPOAb), and free thyroxine (FT4) were assessed by chemiluminescence; the Toxoplasma status was detected by the complement fixation test (CFT) and anti-Toxoplasma IgG enzyme-linked immunosorbent assay (ELISA). Results Overall, 22.5% of the women were positive for latent toxoplasmosis and 14.7% were screened positive for AITD. Women with latent toxoplasmosis had more often highly elevated TPOAb than the Toxoplasma-negative ones (p = 0.004), and latent toxoplasmosis was associated with decrease in serum TSH levels (p = 0.049). Moreover, we found a positive correlation between FT4 and the index of positivity for anti-Toxoplasma IgG antibodies (p = 0.033), which was even stronger in the TPOAb-positive Toxoplasma-positive women, (p = 0.014), as well as a positive correlation between FT4 and log2 CFT (p = 0.009). Conclusions Latent toxoplasmosis was associated with a mild increase in thyroid hormone production in pregnancy. The observed Toxoplasma-associated changes in the parameters of AITD are mild and do not seem to be clinically relevant; however, they could provide new clues to the complex pathogenesis of autoimmune thyroid diseases. PMID:25350671

  9. Effects of latent toxoplasmosis on autoimmune thyroid diseases in pregnancy.

    PubMed

    Kaňková, Šárka; Procházková, Lucie; Flegr, Jaroslav; Calda, Pavel; Springer, Drahomíra; Potluková, Eliška

    2014-01-01

    Toxoplasmosis, one of the most common zoonotic diseases worldwide, can induce various hormonal and behavioural alterations in infected hosts, and its most common form, latent toxoplasmosis, influences the course of pregnancy. Autoimmune thyroid diseases (AITD) belong to the well-defined risk factors for adverse pregnancy outcomes. The aim of this study was to investigate whether there is a link between latent toxoplasmosis and maternal AITD in pregnancy. Cross-sectional study in 1248 consecutive pregnant women in the 9-12th gestational weeks. Serum thyroid-stimulating hormone (TSH), thyroperoxidase antibodies (TPOAb), and free thyroxine (FT4) were assessed by chemiluminescence; the Toxoplasma status was detected by the complement fixation test (CFT) and anti-Toxoplasma IgG enzyme-linked immunosorbent assay (ELISA). Overall, 22.5% of the women were positive for latent toxoplasmosis and 14.7% were screened positive for AITD. Women with latent toxoplasmosis had more often highly elevated TPOAb than the Toxoplasma-negative ones (p = 0.004), and latent toxoplasmosis was associated with decrease in serum TSH levels (p = 0.049). Moreover, we found a positive correlation between FT4 and the index of positivity for anti-Toxoplasma IgG antibodies (p = 0.033), which was even stronger in the TPOAb-positive Toxoplasma-positive women, (p = 0.014), as well as a positive correlation between FT4 and log2 CFT (p = 0.009). Latent toxoplasmosis was associated with a mild increase in thyroid hormone production in pregnancy. The observed Toxoplasma-associated changes in the parameters of AITD are mild and do not seem to be clinically relevant; however, they could provide new clues to the complex pathogenesis of autoimmune thyroid diseases.

  10. Estimation in Latent Trait Models.

    ERIC Educational Resources Information Center

    Rigdon, Steven E.; Tsutakawa, Robert K.

    Estimation of ability and item parameters in latent trait models is discussed. When both ability and item parameters are considered fixed but unknown, the method of maximum likelihood for the logistic or probit models is well known. Discussed are techniques for estimating ability and item parameters when the ability parameters or item parameters…

  11. Exploiting UMLS semantics for checking semantic consistency among UMLS concepts.

    PubMed

    Erdogan, Halit; Erdem, Esra; Bodenreider, Olivier

    2010-01-01

    To quantify semantic inconsistency in UMLS concepts from the perspective of their hierarchical relations and to show through examples how semantically-inconsistent concepts can help reveal erroneous synonymy relations. Inconsistency is defined in reference to concepts from the UMLS Metathesaurus. Consistency is evaluated by comparing the semantic groups of the two concepts in each pair of hierarchically-related concepts. A limited number of inconsistent concepts was inspected manually. 81,512 concepts are inconsistent due to the differences in semantic groups between a concept and its parent. Four examples of wrong synonymy are presented. A vast majority of inconsistent hierarchical relations are not indicative of any errors. We discovered an interesting semantic pattern along hierarchies, which seems associated with wrong synonymy.

  12. Euphorbia Kansui Reactivates Latent HIV

    PubMed Central

    Cary, Daniele C.; Fujinaga, Koh; Peterlin, B. Matija

    2016-01-01

    While highly active anti-retroviral therapy has greatly improved the lives of HIV infected individuals, these treatments are unable to eradicate the virus. Current approaches to reactivate the virus have been limited by toxicity, lack of an orally available therapy, and limited responses in primary CD4+ T cells and in clinical trials. The PKC agonist ingenol, purified from Euphorbia plants, is a potent T cell activator and reactivates latent HIV. Euphorbia kansui itself has been used for centuries in traditional Chinese medicine to treat ascites, fluid retention, and cancer. We demonstrate that an extract of this plant, Euphorbia kansui, is capable of recapitulating T cell activation induced by the purified ingenol. Indeed, Euphorbia kansui induced expression of the early T cell activation marker CD69 and P-TEFb in a dose-dependent manner. Furthermore, Euphorbia kansui reactivated latent HIV in a CD4+ T cell model of latency and in HIV+ HAART suppressed PBMC. When combined with the other latency reversing agents, the effective dose of Euphorbia kansui required to reactive HIV was reduced 10-fold and resulted in synergistic reactivation of latent HIV. We conclude that Euphorbia Euphorbia kansui reactivates latent HIV and activates CD4+ T cells. When used in combination with a latency reversing agent, the effective dose of Euphorbia kansui is reduced; which suggests its application as a combination strategy to reactivate latent HIV while limiting the toxicity due to global T cell activation. As a natural product, which has been used in traditional medicine for thousands of years, Euphorbia kansui is attractive as a potential treatment strategy, particularly in resource poor countries with limited treatment options. Further clinical testing will be required to determine its safety with current anti-retroviral therapies. PMID:27977742

  13. A Semantics of Synchronization.

    DTIC Science & Technology

    1980-09-01

    AD-AQ91 015 MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR COMPUTE --ETC F/S 9/2 A SEMANTICS OF SYNCHRONIZATION.(U) .C SEP 80 C A SEAQUIST N00015-75... COMPUTER SCIENCE TECHNOLOGY LEVL NIT/WSAIM-176 A SEMW30~CS OF SYNilUMZfTIMt DTIC ELEOTEW- OCT 30 198 Carl R. Seaquist j Septet~e3.980 C..)1tds research... coud ition$c rcatcO)I) end create silrtread =proc(rn:cvt) if inhbusy theci ondifion~wait(an.rcaders) end rn.readcrcou nt: =in.rcadcrcount + 1 comtll ion

  14. Large-margin predictive latent subspace learning for multiview data analysis.

    PubMed

    Chen, Ning; Zhu, Jun; Sun, Fuchun; Xing, Eric Poe

    2012-12-01

    Learning salient representations of multiview data is an essential step in many applications such as image classification, retrieval, and annotation. Standard predictive methods, such as support vector machines, often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies, coherence, and complementarity that offer key insights to the semantics of the data, and are therefore offering weak performance and are incapable of supporting view-level analysis. This paper presents a statistical method to learn a predictive subspace representation underlying multiple views, leveraging both multiview dependencies and availability of supervising side-information. Our approach is based on a multiview latent subspace Markov network (MN) which fulfills a weak conditional independence assumption that multiview observations and response variables are conditionally independent given a set of latent variables. To learn the latent subspace MN, we develop a large-margin approach which jointly maximizes data likelihood and minimizes a prediction loss on training data. Learning and inference are efficiently done with a contrastive divergence method. Finally, we extensively evaluate the large-margin latent MN on real image and hotel review datasets for classification, regression, image annotation, and retrieval. Our results demonstrate that the large-margin approach can achieve significant improvements in terms of prediction performance and discovering predictive latent subspace representations.

  15. Latent Growth Modeling for Logistic Response Functions

    ERIC Educational Resources Information Center

    Choi, Jaehwa; Harring, Jeffrey R.; Hancock, Gregory R.

    2009-01-01

    Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following…

  16. A Multicomponent Latent Trait Model for Diagnosis

    ERIC Educational Resources Information Center

    Embretson, Susan E.; Yang, Xiangdong

    2013-01-01

    This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait…

  17. A Latent Class Model for Rating Data.

    ERIC Educational Resources Information Center

    Rost, Jurgen

    1985-01-01

    A latent class model for rating data is presented which provides an alternative to the latent trait approach of analyzing test data. It is the analog of Andrich's binomial Rasch model for Lazarsfeld's latent class analysis (LCA). Response probabilities for rating categories follow a binomial distribution and depend on class-specific item…

  18. Latent Growth Modeling for Logistic Response Functions

    ERIC Educational Resources Information Center

    Choi, Jaehwa; Harring, Jeffrey R.; Hancock, Gregory R.

    2009-01-01

    Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following…

  19. Information tables with neighborhood semantics

    NASA Astrophysics Data System (ADS)

    Yao, Yiyu

    2000-04-01

    Information tables provide a convenient and useful tool for representing a set of objects using a group of attributes. This notion is enriched by introducing neighborhood systems on attribute values. The neighborhood systems represent the semantics relationships between, and knowledge about, attribute values. With added semantics, neighborhood based information tables may provide a more general framework for knowledge discovery, data mining, and information retrieval.

  20. Semantic Research for Digital Libraries.

    ERIC Educational Resources Information Center

    Chen, Hsinchun

    1999-01-01

    Discusses the need for semantic research in digital libraries to help overcome interoperability problems. Highlights include federal initiatives; semantic analysis; knowledge representations; human-computer interactions and information visualization; and the University of Illinois DLI (Digital Libraries Initiative) project through partnership with…

  1. 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…

  2. 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…

  3. The semantic planetary data system

    NASA Technical Reports Server (NTRS)

    Hughes, J. Steven; Crichton, Daniel; Kelly, Sean; Mattmann, Chris

    2005-01-01

    This paper will provide a brief overview of the PDS data model and the PDS catalog. It will then describe the implentation of the Semantic PDS including the development of the formal ontology, the generation of RDFS/XML and RDF/XML data sets, and the buiding of the semantic search application.

  4. [Semantic information. Internal language. Thinking].

    PubMed

    Azcoaga, J E

    1993-06-01

    Semantic information has reached an objective condition after a lengthy history of semantic inquiries that instrumental neurophysiological devices--such as event-related potentials, electroencephalographic spectral analysis, regional brain circulation, PET scan, deep brain electrodes, and other--have made easier. In turn, internal language, as screened according to Vigotsky's perspective, is considered a product of semantic information circulation understood as neurosemae interconnection. Finally, in normal adults, thinking processes are assumed to be made up by both sensoperceptive information (proprioceptive information included) and semantic information. Thus, an "extraverbal thinking" can be distinguished, whose activity is hardly describable in healthy adults but should be considered as a condition of non-educated deaf persons, and a "verbal thinking", or internal language, made up by semantic information.

  5. Semantic Workflows and Provenance

    NASA Astrophysics Data System (ADS)

    Gil, Y.

    2011-12-01

    While sharing and disseminating data is widely practiced across scientific communities, we have yet to recognize the importance of sharing and disseminating the analytic processes that leads to published data. Data retrieved from shared repositories and archives is often hard to interpret because we lack documentation about those processes: what models were used, what assumptions were made, what calibrations were carried out, etc. This process documentation is also key to aggregate data in a meaningful way, whether aggregating shared third party data or aggregating shared data with local sensor data collected by individual investigators. We suggest that augmenting published data with process documentation would greatly enhance our ability to find, reuse, interpret, and aggregate data and therefore have a significant impact in the utility of data repositories and archives. We will show that semantic workflows and provenance provide key technologies for capturing process documentation. Semantic workflows describe the kinds of data transformation and analysis steps used to create new data products, and can include useful constraints about why specific models were selected or parameters chosen. Provenance records can be used to publish workflow descriptions in standard formats that can be reused to enable verification and reproducibility of data products.

  6. Distributed Semantic Overlay Networks

    NASA Astrophysics Data System (ADS)

    Doulkeridis, Christos; Vlachou, Akrivi; Nørvåg, Kjetil; Vazirgiannis, Michalis

    Semantic Overlay Networks (SONs) have been recently proposed as a way to organize content in peer-to-peer (P2P) networks. The main objective is to discover peers with similar content and then form thematically focused peer groups. Efficient content retrieval can be performed by having queries selectively forwarded only to relevant groups of peers to the query. As a result, less peers need to be contacted, in order to answer a query. In this context, the challenge is to generate SONs in a decentralized and distributed manner, as the centralized assembly of global information is not feasible. Different approaches for exploiting the generated SONs for content retrieval have been proposed in the literature, which are examined in this chapter, with a particular focus on SON interconnections for efficient search. Several applications, such as P2P document and image retrieval, can be deployed over generated SONs, motivating the need for distributed and truly scalable SON creation. Therefore, recently several research papers focus on SONs as stated in our comprehensive overview of related work in the field of semantic overlay networks. A classification of existing algorithms according to a set of qualitative criteria is also provided. In spite of the rich existing work in the field of SONs, several challenges have not been efficiently addressed yet, therefore, future promising research directions are pointed out and discussed at the end of this chapter.

  7. Semantics and pragmatics.

    PubMed

    McNally, Louise

    2013-05-01

    The fields of semantics and pragmatics are devoted to the study of conventionalized and context- or use-dependent aspects of natural language meaning, respectively. The complexity of human language as a semiotic system has led to considerable debate about how the semantics/pragmatics distinction should be drawn, if at all. This debate largely reflects contrasting views of meaning as a property of linguistic expressions versus something that speakers do. The fact that both views of meaning are essential to a complete understanding of language has led to a variety of efforts over the last 40 years to develop better integrated and more comprehensive theories of language use and interpretation. The most important advances have included the adaptation of propositional analyses of declarative sentences to interrogative, imperative and exclamative forms; the emergence of dynamic, game theoretic, and multi-dimensional theories of meaning; and the development of various techniques for incorporating context-dependent aspects of content into representations of context-invariant content with the goal of handling phenomena such as vagueness resolution, metaphor, and metonymy. WIREs Cogn Sci 2013, 4:285-297. doi: 10.1002/wcs.1227 For further resources related to this article, please visit the WIREs website. The authors declare no conflict of interest. Copyright © 2013 John Wiley & Sons, Ltd.

  8. Latent class model characterization of neighborhood socioeconomic status

    PubMed Central

    Michael, Yvonne; Hyslop, Terry

    2016-01-01

    Purpose Neighborhood-level socioeconomic status (NSES) can influence breast cancer mortality and poorer health outcomes are observed in deprived neighborhoods. Commonly used NSES indexes are difficult to interpret. Latent class models allow for alternative characterization of NSES for use in studies of cancer causes and control. Methods Breast cancer data was from a cohort of women diagnosed at an academic medical center in Philadelphia, PA. NSES variables were defined using Census data. Latent class modeling was used to characterize NSES. Results Complete data was available for 1,664 breast cancer patients diagnosed between 1994 and 2002. Two separate latent variables, each with 2-classes (LC2) best represented NSES. LC2 demonstrated strong associations with race and tumor stage and size. Conclusions Latent variable models identified specific characteristics associated with advantaged or disadvantaged neighborhoods, potentially improving our understanding of the impact of socioeconomic influence on breast cancer prognosis. Improved classification will enhance our ability to identify vulnerable populations and prioritize the targeting of cancer control efforts. PMID:26797452

  9. Latent period in clinical radiation myelopathy

    SciTech Connect

    Schultheiss, T.E.; Higgins, E.M.; El-Mahdi, A.M.

    1984-07-01

    Seventy-seven papers containing data on more than 300 cases of radiation myelopathy have been analyzed. The data suggest that the latent periods are similar in the cervical and thoracic levels of the spinal cord and are bimodally distributed. Myelopathy of lumbar cord apparently has a shorter latent period. As in controlled animal experiments, the latent period decreases with increasing dose. Furthermore, the variation in latent periods also decreases with dose. It is also seen that retreated patients and pediatric or adolescent patients have greatly reduced latent periods. The implications of these findings as they compare with the animal data are discussed.

  10. "Pre-Semantic" Cognition Revisited: Critical Differences between Semantic Aphasia and Semantic Dementia

    ERIC Educational Resources Information Center

    Jefferies, Elizabeth; Rogers, Timothy T.; Hopper, Samantha; Lambon Ralph, Matthew A.

    2010-01-01

    Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal "pre-semantic" tasks, e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are…

  11. "Pre-Semantic" Cognition Revisited: Critical Differences between Semantic Aphasia and Semantic Dementia

    ERIC Educational Resources Information Center

    Jefferies, Elizabeth; Rogers, Timothy T.; Hopper, Samantha; Lambon Ralph, Matthew A.

    2010-01-01

    Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal "pre-semantic" tasks, e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are…

  12. 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

  13. High density ERP indices of conscious and unconscious semantic priming.

    PubMed

    Ruz, María; Madrid, Eduardo; Lupiáñez, Juan; Tudela, Pío

    2003-10-01

    The existence of differential brain mechanisms of conscious and unconscious processing is a matter of debate nowadays. The present experiment explores whether conscious and unconscious semantic priming in a lexical decision task at a long prime-target stimulus onset asynchrony (SOA) correlate with overlapping or different event related potential (ERP) effects. Results show that the N400 effect, which appeared when words were consciously perceived, completely disappeared when primes were masked at a level where the ability of participants to detect the prime was near chance. Instead, a rather different set of ERP effects was found to index unconscious semantic priming. This suggests that the processes at the basis of conscious and unconscious semantic analyses can under some circumstances be rather different. Moreover, our results support the notion that conscious and unconscious processes are at least partially separable in the brain.

  14. Effects of adult aging on utilization of temporal and semantic associations during free and serial recall

    PubMed Central

    Golomb, Julie D.; Peelle, Jonathan E.; Addis, Kelly M.; Kahana, Michael J.; Wingfield, Arthur

    2009-01-01

    Older adults show poorer performance than young adults at word list recall, especially for order information. In contrast with this temporal association deficit, older adults are generally adept at using preexisting semantic associations, when present, to aid recall. We compared the use of temporal and semantic associations in young and older adults’ word list recall following both free recall and serial recall instructions. Decomposition of serial position curves confirmed that older adults showed weakened use of temporal context in recall in relation to young adults, a difference that was amplified in serial recall. Older adults’ temporal associations were also less effective than young adults’ when correlated with serial recall performance. The differential age decrement for serial versus free recall was accompanied by a persistent influence of latent semantic associations in the older adults, even when maladaptive for serial recall. PMID:18630201

  15. Tuberculosis Infection and Latent Tuberculosis

    PubMed Central

    2016-01-01

    Active tuberculosis (TB) has a greater burden of TB bacilli than latent TB and acts as an infection source for contacts. Latent tuberculosis infection (LTBI) is the state in which humans are infected with Mycobacterium tuberculosis without any clinical symptoms, radiological abnormality, or microbiological evidence. TB is transmissible by respiratory droplet nucleus of 1–5 µm in diameter, containing 1–10 TB bacilli. TB transmission is affected by the strength of the infectious source, infectiousness of TB bacilli, immunoresistance of the host, environmental stresses, and biosocial factors. Infection controls to reduce TB transmission consist of managerial activities, administrative control, engineering control, environmental control, and personal protective equipment provision. However, diagnosis and treatment for LTBI as a national TB control program is an important strategy on the precondition that active TB is not missed. Therefore, more concrete evidences for LTBI management based on clinical and public perspectives are needed. PMID:27790271

  16. The Ontological Perspectives of the Semantic Web and the Metadata Harvesting Protocol: Applications of Metadata for Improving Web Search.

    ERIC Educational Resources Information Center

    Fast, Karl V.; Campbell, D. Grant

    2001-01-01

    Compares the implied ontological frameworks of the Open Archives Initiative Protocol for Metadata Harvesting and the World Wide Web Consortium's Semantic Web. Discusses current search engine technology, semantic markup, indexing principles of special libraries and online databases, and componentization and the distinction between data and…

  17. The Ontological Perspectives of the Semantic Web and the Metadata Harvesting Protocol: Applications of Metadata for Improving Web Search.

    ERIC Educational Resources Information Center

    Fast, Karl V.; Campbell, D. Grant

    2001-01-01

    Compares the implied ontological frameworks of the Open Archives Initiative Protocol for Metadata Harvesting and the World Wide Web Consortium's Semantic Web. Discusses current search engine technology, semantic markup, indexing principles of special libraries and online databases, and componentization and the distinction between data and…

  18. 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

  19. A Joint Investigation of Semantic Facilitation and Semantic Interference in Continuous Naming

    ERIC Educational Resources Information Center

    Scaltritti, Michele; Peressotti, Francesca; Navarrete, Eduardo

    2017-01-01

    When speakers name multiple semantically related items, opposing effects can be found. Semantic facilitation is found when naming 2 semantically related items in a row. In contrast, semantic interference is found when speakers name semantically related items separated by 1 or more intervening unrelated items. This latter form of interference is…

  20. The Semantic Distance Model of Relevance Assessment.

    ERIC Educational Resources Information Center

    Brooks, Terrence A.

    1998-01-01

    Presents the Semantic Distance Model (SDM) of Relevance Assessment, a cognitive model of the relationship between semantic distance and relevance assessment. Discusses premises of the model such as the subjective nature of information and the metaphor of semantic distance. Empirical results illustrate the effects of semantic distance and semantic…

  1. Semantic Representation and Naming in Young Children.

    ERIC Educational Resources Information Center

    McGregor, Karla K.; Friedman, Rena M.; Reilly, Renee M.; Newman, Robyn M.

    2002-01-01

    Two experiments examined children's semantic representations and semantic naming errors. Results suggested that functional and physical properties are core aspects of object representations in the semantic lexicon and that the degree of semantic knowledge makes words more or less vulnerable to retrieval failure. Discussion focuses on the dynamic…

  2. Mapping the Structure of Semantic Memory

    ERIC Educational Resources Information Center

    Morais, Ana Sofia; Olsson, Henrik; Schooler, Lael J.

    2013-01-01

    Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual's semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals…

  3. The Semantic Distance Model of Relevance Assessment.

    ERIC Educational Resources Information Center

    Brooks, Terrence A.

    1998-01-01

    Presents the Semantic Distance Model (SDM) of Relevance Assessment, a cognitive model of the relationship between semantic distance and relevance assessment. Discusses premises of the model such as the subjective nature of information and the metaphor of semantic distance. Empirical results illustrate the effects of semantic distance and semantic…

  4. Mapping the Structure of Semantic Memory

    ERIC Educational Resources Information Center

    Morais, Ana Sofia; Olsson, Henrik; Schooler, Lael J.

    2013-01-01

    Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual's semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals…

  5. Latent heat of vehicular motion

    NASA Astrophysics Data System (ADS)

    Ahmadi, Farzad; Berrier, Austin; Habibi, Mohammad; Boreyko, Jonathan

    2016-11-01

    We have used the thermodynamic concept of latent heat, where a system loses energy due to a solid-to-liquid phase transition, to study the flow of a group of vehicles moving from rest. During traffic flow, drivers keep a large distance from the car in front of them to ensure safe driving. When a group of cars comes to a stop, for example at a red light, drivers voluntarily induce a "phase transition" from this "liquid phase" to a close-packed "solid phase." This phase transition is motivated by the intuition that maximizing displacement before stopping will minimize the overall travel time. To test the effects of latent heat on flow efficiency, a drone captured the dynamics of cars flowing through an intersection on a Smart Road where the initial spacing between cars at the red light was systematically varied. By correlating the experimental results with the Optimal Velocity Model (OVM), we find that the convention of inducing phase transitions at intersections offers no benefit, as the lag time (latent heat) of resumed flow offsets the initial increase in displacement. These findings suggest that in situations where gridlock is not an issue, drivers should not decrease their spacing during stoppages in order to maximize safety with no loss in flow efficiency.

  6. 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.

  7. Exploiting Recurring Structure in a Semantic Network

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, Richard M.

    2004-01-01

    With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

  8. Exploiting Recurring Structure in a Semantic Network

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, Richard M.

    2004-01-01

    With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

  9. Category specific semantic impairments.

    PubMed

    Warrington, E K; Shallice, T

    1984-09-01

    We report a quantitative investigation of the visual identification and auditory comprehension deficits of 4 patients who had made a partial recovery from herpes simplex encephalitis. Clinical observations had suggested the selective impairment and selective preservation of certain categories of visual stimuli. In all 4 patients a significant discrepancy between their ability to identify inanimate objects and inability to identify living things and foods was demonstrated. In 2 patients it was possible to compare visual and verbal modalities and the same pattern of dissociation was observed in both. For 1 patient, comprehension of abstract words was significantly superior to comprehension of concrete words. Consistency of responses was recorded within a modality in contrast to a much lesser degree of consistency between modalities. We interpret our findings in terms of category specificity in the organization of meaning systems that are also modality specific semantic systems.

  10. Workspaces in the Semantic Web

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, RIchard M.

    2005-01-01

    Due to the recency and relatively limited adoption of Semantic Web technologies. practical issues related to technology scaling have received less attention than foundational issues. Nonetheless, these issues must be addressed if the Semantic Web is to realize its full potential. In particular, we concentrate on the lack of scoping methods that reduce the size of semantic information spaces so they are more efficient to work with and more relevant to an agent's needs. We provide some intuition to motivate the need for such reduced information spaces, called workspaces, give a formal definition, and suggest possible methods of deriving them.

  11. High Performance Descriptive Semantic Analysis of Semantic Graph Databases

    SciTech Connect

    Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan; Feo, John T.; Haglin, David J.; Mackey, Greg E.; Mizell, David W.

    2011-06-02

    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 comprising 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.

  12. Does semantic redundancy gain result from multiple semantic priming?

    PubMed

    Schröter, Hannes; Bratzke, Daniel; Fiedler, Anja; Birngruber, Teresa

    2015-10-01

    Fiedler, Schröter, and Ulrich (2013) reported faster responses to a single written word when the semantic content of this word (e.g., "elephant") matched both targets (e.g., "animal", "gray") as compared to a single target (e.g., "animal", "brown"). This semantic redundancy gain was explained by statistical facilitation due to a race of independent memory retrieval processes. The present experiment addresses one alternative explanation, namely that semantic redundancy gain results from multiple pre-activation of words that match both targets. In different blocks of trials, participants performed a redundant-targets task and a lexical decision task. The targets of the redundant-targets task served as primes in the lexical decision task. Replicating the findings of Fiedler et al., a semantic redundancy gain was observed in the redundant-targets task. Crucially, however, there was no evidence of a multiple semantic priming effect in the lexical decision task. This result suggests that semantic redundancy gain cannot be explained by multiple pre-activation of words that match both targets.

  13. Nine Principles of Semantic Harmonization

    PubMed Central

    Cunningham, James A.; Van Speybroeck, Michel; Kalra, Dipak; Verbeeck, Rudi

    2016-01-01

    Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort’s area of focus. Guided by work we have undertaken in developing the ‘EMIF Knowledge Object Library’, a semantic harmonization framework underpinning the collation of pan-European Alzheimer’s cohort data, we have developed a set of nine generic guiding principles for developing semantic harmonization frameworks, the application of which will establish a solid base for constructing similar frameworks. PMID:28269840

  14. Nine Principles of Semantic Harmonization.

    PubMed

    Cunningham, James A; Van Speybroeck, Michel; Kalra, Dipak; Verbeeck, Rudi

    2016-01-01

    Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort's area of focus. Guided by work we have undertaken in developing the 'EMIF Knowledge Object Library', a semantic harmonization framework underpinning the collation of pan-European Alzheimer's cohort data, we have developed a set of nine generic guiding principles for developing semantic harmonization frameworks, the application of which will establish a solid base for constructing similar frameworks.

  15. Problem Solving with General Semantics.

    ERIC Educational Resources Information Center

    Hewson, David

    1996-01-01

    Discusses how to use general semantics formulations to improve problem solving at home or at work--methods come from the areas of artificial intelligence/computer science, engineering, operations research, and psychology. (PA)

  16. Distributed semantic networks and CLIPS

    NASA Technical Reports Server (NTRS)

    Snyder, James; Rodriguez, Tony

    1991-01-01

    Semantic networks of frames are commonly used as a method of reasoning in many problems. In most of these applications the semantic network exists as a single entity in a single process environment. Advances in workstation hardware provide support for more sophisticated applications involving multiple processes, interacting in a distributed environment. In these applications the semantic network may well be distributed over several concurrently executing tasks. This paper describes the design and implementation of a frame based, distributed semantic network in which frames are accessed both through C Language Integrated Production System (CLIPS) expert systems and procedural C++ language programs. The application area is a knowledge based, cooperative decision making model utilizing both rule based and procedural experts.

  17. Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

    NASA Astrophysics Data System (ADS)

    Möller, Manuel; Sintek, Michael; Buitelaar, Paul; Mukherjee, Saikat; Zhou, Xiang Sean; Freund, Jörg

    Recent advances in medical imaging technology have dramatically increased the amount of clinical image data. In contrast, techniques for efficiently exploiting the rich semantic information in medical images have evolved much slower. Despite the research outcomes in image understanding, current image databases are still indexed by manually assigned subjective keywords instead of the semantics of the images. Indeed, most current content-based image search applications index image features that do not generalize well and use inflexible queries. This slow progress is due to the lack of scalable and generic information representation systems which can abstract over the high dimensional nature of medical images as well as semantically model the results of object recognition techniques. We propose a system combining medical imaging information with ontological formalized semantic knowledge that provides a basis for building universal knowledge repositories and gives clinicians fully cross-lingual and cross-modal access to biomedical information.

  18. Semantic Interoperability on the Web

    DTIC Science & Technology

    2000-01-01

    these agents would not be affected by presentation changes if the pages were available in XML, they would still break if the XML representation of the... these semantics into tools that are used to interpret or translate the XML documents, but software tools cannot acquire these semantics independently...mapping differences in naming conventions. As with natural language, XML DTDs have the problems of polysemy and synonymy. For example, the elements

  19. NASA and The Semantic Web

    NASA Technical Reports Server (NTRS)

    Ashish, Naveen

    2005-01-01

    We provide an overview of several ongoing NASA endeavors based on concepts, systems, and technology from the Semantic Web arena. Indeed NASA has been one of the early adopters of Semantic Web Technology and we describe ongoing and completed R&D efforts for several applications ranging from collaborative systems to airspace information management to enterprise search to scientific information gathering and discovery systems at NASA.

  20. Neural substrates of semantic memory.

    PubMed

    Hart, John; Anand, Raksha; Zoccoli, Sandra; Maguire, Mandy; Gamino, Jacque; Tillman, Gail; King, Richard; Kraut, Michael A

    2007-09-01

    Semantic memory is described as the storage of knowledge, concepts, and information that is common and relatively consistent across individuals (e.g., memory of what is a cup). These memories are stored in multiple sensorimotor modalities and cognitive systems throughout the brain (e.g., how a cup is held and manipulated, the texture of a cup's surface, its shape, its function, that is related to beverages such as coffee, and so on). Our ability to engage in purposeful interactions with our environment is dependent on the ability to understand the meaning and significance of the objects and actions around us that are stored in semantic memory. Theories of the neural basis of the semantic memory of objects have produced sophisticated models that have incorporated to varying degrees the results of cognitive and neural investigations. The models are grouped into those that are (1) cognitive models, where the neural data are used to reveal dissociations in semantic memory after a brain lesion occurs; (2) models that incorporate both cognitive and neuroanatomical information; and (3) models that use cognitive, neuroanatomic, and neurophysiological data. This review highlights the advances and issues that have emerged from these models and points to future directions that provide opportunities to extend these models. The models of object memory generally describe how category and/or feature representations encode for object memory, and the semantic operations engaged in object processing. The incorporation of data derived from multiple modalities of investigation can lead to detailed neural specifications of semantic memory organization. The addition of neurophysiological data can potentially provide further elaboration of models to include semantic neural mechanisms. Future directions should incorporate available and newly developed techniques to better inform the neural underpinning of semantic memory models.

  1. 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).

  2. Latent variable modeling of cognitive processes in true and false recognition of words: A developmental perspective.

    PubMed

    Bouwmeester, Samantha; Verkoeijen, Peter P J L

    2010-05-01

    The present study aimed at testing theoretical predictions of the fuzzy-trace theory about true and false recognition. The effects of semantic relatedness and study opportunity on true and false recognition of words from Deese, Roediger, McDermott lists (J. Deese, 1959; D. R. Read, 1996; H. L. Roediger & K. B. McDermott, 1995) were evaluated in 7- to 12-year-old children (N = 151). Instead of a traditional analysis of variance, the authors used a relatively novel statistical analysis technique, latent class factor analysis, to test the hypotheses pertaining to the effect of semantic relatedness and study opportunity on children's true and false recognition given their low or high verbatim-trace and gist-trace level. The results showed that variation in true recognition of target words from semantically related and unrelated word lists that were either studied once or repeated could be explained well by variation in verbatim-trace and gist-trace level. Variation in false recognition of semantically related distractors also could be explained by variation in gist-trace level, but the recollection-rejection hypothesis was not confirmed. The variable age was positively but weakly related to gist-trace level, but no significant relationship was found between age and verbatim-trace level.

  3. WORD STATISTICS IN THE GENERATION OF SEMANTIC TOOLS FOR INFORMATION SYSTEMS.

    ERIC Educational Resources Information Center

    STONE, DON C.

    ONE OF THE PROBLEMS IN INFORMATION STORAGE AND RETRIEVAL SYSTEMS OF TECHNICAL DOCUMENTS IS THE INTERPRETATION OF WORDS USED TO INDEX DOCUMENTS. SEMANTIC TOOLS, DEFINED AS CHANNELS FOR THE COMMUNICATION OF WORD MEANINGS BETWEEN TECHNICAL EXPERTS, DOCUMENT INDEXERS, AND SEARCHERS, PROVIDE ONE METHOD OF DEALING WITH THE PROBLEM OF MULTIPLE…

  4. Bayesian variable selection for latent class models.

    PubMed

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  5. Latent Heating from TRMM Satellite Measurements

    NASA Astrophysics Data System (ADS)

    Tao, W.; Takayabu, Y. N.; Shige, S.; Lang, S. E.; Olson, W. S.

    2012-12-01

    Rainfall production is a fundamental process within the Earth's hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has been developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generated from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  6. Tracking the dynamics of divergent thinking via semantic distance: Analytic methods and theoretical implications.

    PubMed

    Hass, Richard W

    2017-02-01

    Divergent thinking has often been used as a proxy measure of creative thinking, but this practice lacks a foundation in modern cognitive psychological theory. This article addresses several issues with the classic divergent-thinking methodology and presents a new theoretical and methodological framework for cognitive divergent-thinking studies. A secondary analysis of a large dataset of divergent-thinking responses is presented. Latent semantic analysis was used to examine the potential changes in semantic distance between responses and the concept represented by the divergent-thinking prompt across successive response iterations. The results of linear growth modeling showed that although there is some linear increase in semantic distance across response iterations, participants high in fluid intelligence tended to give more distant initial responses than those with lower fluid intelligence. Additional analyses showed that the semantic distance of responses significantly predicted the average creativity rating given to the response, with significant variation in average levels of creativity across participants. Finally, semantic distance does not seem to be related to participants' choices of their own most creative responses. Implications for cognitive theories of creativity are discussed, along with the limitations of the methodology and directions for future research.

  7. Indexing Learning Objects: Vocabularies and Empirical Investigation of Consistency

    ERIC Educational Resources Information Center

    Kabel, Suzanne; De Hoog, Robert; Wielinga, Bob; Anjewierden, Anjo

    2004-01-01

    In addition to the LOM standard and instructional design specifications, as well as domain specific indexing vocabularies, a structured indexing vocabulary for the more elementary learning objects is advisable in order to support retrieval tasks of developers. Furthermore, because semantic indexing is seen as a difficult task, three issues…

  8. Indexing Learning Objects: Vocabularies and Empirical Investigation of Consistency

    ERIC Educational Resources Information Center

    Kabel, Suzanne; De Hoog, Robert; Wielinga, Bob; Anjewierden, Anjo

    2004-01-01

    In addition to the LOM standard and instructional design specifications, as well as domain specific indexing vocabularies, a structured indexing vocabulary for the more elementary learning objects is advisable in order to support retrieval tasks of developers. Furthermore, because semantic indexing is seen as a difficult task, three issues…

  9. Estimating and Interpreting Latent Variable Interactions: A Tutorial for Applying the Latent Moderated Structural Equations Method

    ERIC Educational Resources Information Center

    Maslowsky, Julie; Jager, Justin; Hemken, Douglas

    2015-01-01

    Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS)…

  10. Information-Theoretic Latent Distribution Modeling: Distinguishing Discrete and Continuous Latent Variable Models

    ERIC Educational Resources Information Center

    Markon, Kristian E.; Krueger, Robert F.

    2006-01-01

    Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed…

  11. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    ERIC Educational Resources Information Center

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  12. Estimating and Interpreting Latent Variable Interactions: A Tutorial for Applying the Latent Moderated Structural Equations Method

    ERIC Educational Resources Information Center

    Maslowsky, Julie; Jager, Justin; Hemken, Douglas

    2015-01-01

    Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS)…

  13. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    ERIC Educational Resources Information Center

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  14. The Latent Structure of Autistic Traits: A Taxometric, Latent Class and Latent Profile Analysis of the Adult Autism Spectrum Quotient

    ERIC Educational Resources Information Center

    James, Richard J.; Dubey, Indu; Smith, Danielle; Ropar, Danielle; Tunney, Richard J.

    2016-01-01

    Autistic traits are widely thought to operate along a continuum. A taxometric analysis of Adult Autism Spectrum Quotient data was conducted to test this assumption, finding little support but identifying a high severity taxon. To understand this further, latent class and latent profile models were estimated that indicated the presence of six…

  15. Semi-Nonparametric Methods for Detecting Latent Non-Normality: A Fusion of Latent Trait and Ordered Latent Class Modeling

    ERIC Educational Resources Information Center

    Schmitt, J. Eric; Mehta, Paras D.; Aggen, Steven H.; Kubarych, Thomas S.; Neale, Michael C.

    2006-01-01

    Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation,…

  16. The Latent Structure of Autistic Traits: A Taxometric, Latent Class and Latent Profile Analysis of the Adult Autism Spectrum Quotient

    ERIC Educational Resources Information Center

    James, Richard J.; Dubey, Indu; Smith, Danielle; Ropar, Danielle; Tunney, Richard J.

    2016-01-01

    Autistic traits are widely thought to operate along a continuum. A taxometric analysis of Adult Autism Spectrum Quotient data was conducted to test this assumption, finding little support but identifying a high severity taxon. To understand this further, latent class and latent profile models were estimated that indicated the presence of six…

  17. Semi-Nonparametric Methods for Detecting Latent Non-Normality: A Fusion of Latent Trait and Ordered Latent Class Modeling

    ERIC Educational Resources Information Center

    Schmitt, J. Eric; Mehta, Paras D.; Aggen, Steven H.; Kubarych, Thomas S.; Neale, Michael C.

    2006-01-01

    Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation,…

  18. Semantic Similarities between a Keyword Database and a Controlled Vocabulary Database: An Investigation in the Antibiotic Resistance Literature.

    ERIC Educational Resources Information Center

    Qin, Jian

    2000-01-01

    Explores similarities or dissimilarities between citation-semantic and analytic indexing based on a study of records in the Science Citation Index and MEDLINE databases on antibiotic resistance in pneumonia. Concludes that disparate indexing terms may be an advantage for better recall and precision in information retrieval. (Contains 42…

  19. Semantic Similarities between a Keyword Database and a Controlled Vocabulary Database: An Investigation in the Antibiotic Resistance Literature.

    ERIC Educational Resources Information Center

    Qin, Jian

    2000-01-01

    Explores similarities or dissimilarities between citation-semantic and analytic indexing based on a study of records in the Science Citation Index and MEDLINE databases on antibiotic resistance in pneumonia. Concludes that disparate indexing terms may be an advantage for better recall and precision in information retrieval. (Contains 42…

  20. Orienting attention to semantic categories

    PubMed Central

    Cristescu, Tamara C.; Devlin, Joseph T.; Nobre, Anna C.

    2006-01-01

    We investigated the ability to orient attention to a complex, non-perceptual attribute of stimuli—semantic category. Behavioral consequences and neural correlates of semantic orienting were revealed and compared with those of spatial orienting, using event-related functional magnetic-resonance imaging. Semantic orienting significantly shortened response times to identify word stimuli, showing that it is possible to focus attention on non-perceptual attributes of stimuli to enhance behavioral performance. Semantic-orienting cues engaged parietal and frontal areas that were also involved in spatial orienting, but in addition engaged brain areas associated with semantic analysis of words, such as the left anterior inferior frontal cortex. These findings show that attentional orienting selectively engages brain areas with functional specialization for the predicted attributes. They also support the existence of a core frontoparietal network, which controls attentional orienting in speeded response tasks independently of the type of expectations, interacting with task-relevant functionally specialized areas to optimize perception and action. PMID:17011212

  1. Consequences of Fitting Nonidentified Latent Class Models

    ERIC Educational Resources Information Center

    Abar, Beau; Loken, Eric

    2012-01-01

    Latent class models are becoming more popular in behavioral research. When models with a large number of latent classes relative to the number of manifest indicators are estimated, researchers must consider the possibility that the model is not identified. It is not enough to determine that the model has positive degrees of freedom. A well-known…

  2. Latent Memory for Sensitization in "Aplysia"

    ERIC Educational Resources Information Center

    Philips, Gary T.; Tzvetkova, Ekaterina I.; Marinesco, Stephane; Carew, Thomas J.

    2006-01-01

    In the analysis of memory it is commonly observed that, even after a memory is apparently forgotten, its latent presence can still be revealed in a subsequent learning task. Although well established on a behavioral level, the mechanisms underlying latent memory are not well understood. To begin to explore these mechanisms, we have used "Aplysia,"…

  3. Latent Memory for Sensitization in "Aplysia"

    ERIC Educational Resources Information Center

    Philips, Gary T.; Tzvetkova, Ekaterina I.; Marinesco, Stephane; Carew, Thomas J.

    2006-01-01

    In the analysis of memory it is commonly observed that, even after a memory is apparently forgotten, its latent presence can still be revealed in a subsequent learning task. Although well established on a behavioral level, the mechanisms underlying latent memory are not well understood. To begin to explore these mechanisms, we have used "Aplysia,"…

  4. [New tests for diagnosis of latent tubercolosis].

    PubMed

    Palumbo, Emilio

    2007-12-01

    Two new tests (QuantiFERON-TB and T-SPOT.TB) for diagnosis of latent tuberculosis are on the market. They measure the release of interferon-gamma in whole blood in response to stimulation by PDD. They offer a more accurate approach than tuberculin skin test for identification of individuals with latent tuberculosis infection.

  5. A Vernacular for Linear Latent Growth Models

    ERIC Educational Resources Information Center

    Hancock, Gregory R.; Choi, Jaehwa

    2006-01-01

    In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…

  6. Cognitive Diagnosis Using Latent Trait Models.

    ERIC Educational Resources Information Center

    Samejima, Fumiko

    This paper discusses the competency space approach to diagnosing misconceptions, skill, and knowledge acquisition. In some approaches that combine misconceptions, skill, and knowledge acquisition, the latent ability theta is used more or less as an insignificant element, but in the competency space approach, a multidimensional latent space is…

  7. Sampling Weights in Latent Variable Modeling

    ERIC Educational Resources Information Center

    Asparouhov, Tihomir

    2005-01-01

    This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The…

  8. Latent Heating from TRMM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Smith, E.; Olson, W.

    2005-01-01

    Rainfall production is a fundamental process within the Earth;s hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations with the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional amount of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the spacetime structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has and continues to be developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generate from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  9. Introduction to Latent Class Analysis with Applications

    ERIC Educational Resources Information Center

    Porcu, Mariano; Giambona, Francesca

    2017-01-01

    Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence…

  10. Introduction to Latent Class Analysis with Applications

    ERIC Educational Resources Information Center

    Porcu, Mariano; Giambona, Francesca

    2017-01-01

    Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence…

  11. NCBO Technology: Powering semantically aware applications.

    PubMed

    Whetzel, Patricia L

    2013-04-15

    As new biomedical technologies are developed, the amount of publically available biomedical data continues to increase. To help manage these vast and disparate data sources, researchers have turned to the Semantic Web. Specifically, ontologies are used in data annotation, natural language processing, information retrieval, clinical decision support, and data integration tasks. The development of software applications to perform these tasks requires the integration of Web services to incorporate the wide variety of ontologies used in the health care and life sciences. The National Center for Biomedical Ontology, a National Center for Biomedical Computing created under the NIH Roadmap, developed BioPortal, which provides access to one of the largest repositories of biomedical ontologies. The NCBO Web services provide programmtic access to these ontologies and can be grouped into four categories; Ontology, Mapping, Annotation, and Data Access. The Ontology Web services provide access to ontologies, their metadata, ontology versions, downloads, navigation of the class hierarchy (parents, children, siblings) and details of each term. The Mapping Web services provide access to the millions of ontology mappings published in BioPortal. The NCBO Annotator Web service "tags" text automatically with terms from ontologies in BioPortal, and the NCBO Resource Index Web services provides access to an ontology-based index of public, online data resources. The NCBO Widgets package the Ontology Web services for use directly in Web sites. The functionality of the NCBO Web services and widgets are incorporated into semantically aware applications for ontology development and visualization, data annotation, and data integration. This overview will describe these classes of applications, discuss a few examples of each type, and which NCBO Web services are used by these applications.

  12. Epstein-Barr virus latent genes.

    PubMed

    Kang, Myung-Soo; Kieff, Elliott

    2015-01-23

    Latent Epstein-Barr virus (EBV) infection has a substantial role in causing many human disorders. The persistence of these viral genomes in all malignant cells, yet with the expression of limited latent genes, is consistent with the notion that EBV latent genes are important for malignant cell growth. While the EBV-encoded nuclear antigen-1 (EBNA-1) and latent membrane protein-2A (LMP-2A) are critical, the EBNA-leader proteins, EBNA-2, EBNA-3A, EBNA-3C and LMP-1, are individually essential for in vitro transformation of primary B cells to lymphoblastoid cell lines. EBV-encoded RNAs and EBNA-3Bs are dispensable. In this review, the roles of EBV latent genes are summarized.

  13. Variable Assessment in Latent Class Models

    PubMed Central

    Zhang, Q.; Ip, E. H.

    2014-01-01

    The latent class model provides an important platform for jointly modeling mixed-mode data — i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures. PMID:24910486

  14. Latent inhibition in human adults without masking.

    PubMed

    Escobar, Martha; Arcediano, Francisco; Miller, Ralph R

    2003-09-01

    Latent inhibition refers to attenuated responding to Cue X observed when the X-outcome pairings are preceded by X-alone presentations. It has proven difficult to obtain in human adults unless the preexposure (X-alone) presentations are embedded within a masking (i.e., distracting) task. The authors hypothesized that the difficulty in obtaining latent inhibition with unmasked tasks is related to the usual training procedures, in which the preexposure and conditioning experiences are separated by a set of instructions. Experiment 1 reports latent inhibition without masking in a task in which preexposure and conditioning occur without interruption. Experiments 2 and 3 demonstrate that this attenuation in responding to target Cue X does not pass a summation test for conditioned inhibition and is context specific, thereby confirming that it is latent inhibition. Experiments 3 and 4 confirm that introducing instructions between preexposure and conditioning disrupts latent inhibition.

  15. Relaxation as a Factor in Semantic Desensitization

    ERIC Educational Resources Information Center

    Bechtel, James E.; McNamara, J. Regis

    1975-01-01

    Relaxation and semantic desensitization were used to alleviate the fear of phobic females. Results showed that semantic desensitization, alone or in combination with relaxation, failed to modify the evaluative meanings evoked by the feared object. (SE)

  16. From N400 to N300: variations in the timing of semantic processing with repetition.

    PubMed

    Renoult, Louis; Wang, Xiaoxiao; Calcagno, Vincent; Prévost, Marie; Debruille, J Bruno

    2012-05-15

    The present study aimed to explore the variations of semantic processing according to the number of target words (i.e., 4, 12 and 24) and according to the number of repetitions (i.e, 1 to 15). The number of targets had no impact on the N400 brain potential, the index of semantic processing, nor on the late positive component (LPC), an index of episodic encoding and retrieval. Analyses of the effects of the number of repetitions showed that the duration of semantic processes--assessed by measuring N400 latency--was linearly shortened along repetitions while their extent--as indexed by N400 amplitude--remained constant after the second presentation. In contrast, the extent of episodic processes--as indexed by LPC amplitude--was found to increase linearly with repetition. By showing that N400 latency may be much less stable than previously thought, these results bring new constraints on the functional correlates of this key stage in the processing of semantic information. They also suggest that semantic processes can be studied at high repetition rates whatever the number of target stimuli. Finally, our findings show that each episode of prior presentation has an impact on the late processing of a stimulus despite the absence of an explicit memory task. Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Treatment of Latent Tuberculosis Infection.

    PubMed

    Haley, Connie A

    2017-04-01

    There are approximately 56 million people who harbor Mycobacterium tuberculosis that may progress to active tuberculosis (TB) at some point in their lives. Modeling studies suggest that if only 8% of these individuals with latent TB infection (LTBI) were treated annually, overall global incidence would be 14-fold lower by 2050 compared to incidence in 2013, even in the absence of additional TB control measures. This highlights the importance of identifying and treating latently infected individuals, and that this intervention must be scaled up to achieve the goals of the Global End TB Strategy. The efficacy of LTBI treatment is well established, and the most commonly used regimen is 9 months of daily self-administered isoniazid. However, its use has been hindered by limited provider awareness of the benefits, concern about potential side effects such as hepatotoxicity, and low rates of treatment completion. There is increasing evidence that shorter rifamycin-based regimens are as effective, better tolerated, and more likely to be completed compared to isoniazid. Such regimens include four months of daily self-administered rifampin monotherapy, three months of once weekly directly observed isoniazid-rifapentine, and three months of daily self-administered isoniazid-rifampin. The success of LTBI treatment to prevent additional TB disease relies upon choosing an appropriate regimen individualized to the patient, monitoring for potential adverse clinical events, and utilizing strategies to promote adherence. Safer, more cost-effective, and more easily completed regimens are needed and should be combined with interventions to better identify, engage, and retain high-risk individuals across the cascade from diagnosis through treatment completion of LTBI.

  18. Semantic processing of crowded stimuli?

    PubMed

    Huckauf, Anke; Knops, Andre; Nuerk, Hans-Christoph; Willmes, Klaus

    2008-11-01

    Effects of semantic processing of crowded characters were investigated using numbers as stimuli. In an identification task, typical spacing effects in crowding were replicated. Using the same stimuli in a magnitude comparison task, a smaller effect of spacing was observed as well as an effect of response congruency. These effects were replicated in a second experiment with varying stimulus-onset asynchronies. In addition, decreasing performance with increasing onset-asynchrony (so-called type-B masking) for incongruent flankers indicates semantic processing of target and flankers. The data show that semantic processing takes place even in crowded stimuli. This argues strongly against common accounts of crowding in terms of early stimulus-driven impairments of processing.

  19. A Semantic Web Blackboard System

    NASA Astrophysics Data System (ADS)

    McKenzie, Craig; Preece, Alun; Gray, Peter

    In this paper, we propose a Blackboard Architecture as a means for coordinating hybrid reasoning over the Semantic Web. We describe the components of traditional blackboard systems (Knowledge Sources, Blackboard, Controller) and then explain how we have enhanced these by incorporating some of the principles of the Semantic Web to pro- duce our Semantic Web Blackboard. Much of the framework is already in place to facilitate our research: the communication protocol (HTTP); the data representation medium (RDF); a rich expressive description language (OWL); and a method of writing rules (SWRL). We further enhance this by adding our own constraint based formalism (CIF/SWRL) into the mix. We provide an example walk-though of our test-bed system, the AKTive Workgroup Builder and Blackboard(AWB+B), illustrating the interaction and cooperation of the Knowledge Sources and providing some context as to how the solution is achieved. We conclude with the strengths and weaknesses of the architecture.

  20. Action semantics modulate action prediction.

    PubMed

    Springer, Anne; Prinz, Wolfgang

    2010-11-01

    Previous studies have demonstrated that action prediction involves an internal action simulation that runs time-locked to the real action. The present study replicates and extends these findings by indicating a real-time simulation process (Graf et al., 2007), which can be differentiated from a similarity-based evaluation of internal action representations. Moreover, results showed that action semantics modulate action prediction accuracy. The semantic effect was specified by the processing of action verbs and concrete nouns (Experiment 1) and, more specifically, by the dynamics described by action verbs (Experiment 2) and the speed described by the verbs (e.g., "to catch" vs. "to grasp" vs. "to stretch"; Experiment 3). These results propose a linkage between action simulation and action semantics as two yet unrelated domains, a view that coincides with a recent notion of a close link between motor processes and the understanding of action language.

  1. Ontology Matching with Semantic Verification

    PubMed Central

    Jean-Mary, Yves R.; Shironoshita, E. Patrick; Kabuka, Mansur R.

    2009-01-01

    ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies. PMID:20186256

  2. Latent Heating from TRMM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Smith, E. A.; Adler, R.; Haddad, Z.; Hou, A.; Iguchi, T.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.

    2004-01-01

    Rainfall production is the fundamental variable within the Earth's hydrological cycle because it is both the principal forcing term in surface water budgets and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the tropics - as well as modifying the energetic efficiencies of midlatitude weather systems. This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global tropics and sub-tropics, information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. The paper examines how the observed TRMM distribution of rainfall has advanced an understanding of the global water and energy cycle and its consequent relationship to the atmospheric general circulation and climate via latent heat release. A set of algorithm methodologies that are being used to estimate latent heating based on rain rate retrievals from the TRMM observations are described. The characteristics of these algorithms and the latent heating products that can be generated from them are also described, along with validation analyses of the heating products themselves. Finally, the investigation provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  3. Effective Web and Desktop Retrieval with Enhanced Semantic Spaces

    NASA Astrophysics Data System (ADS)

    Daoud, Amjad M.

    We describe the design and implementation of the NETBOOK prototype system for collecting, structuring and efficiently creating semantic vectors for concepts, noun phrases, and documents from a corpus of free full text ebooks available on the World Wide Web. Automatic generation of concept maps from correlated index terms and extracted noun phrases are used to build a powerful conceptual index of individual pages. To ensure scalabilty of our system, dimension reduction is performed using Random Projection [13]. Furthermore, we present a complete evaluation of the relative effectiveness of the NETBOOK system versus the Google Desktop [8].

  4. Information Management Meets the Semantic Web

    DTIC Science & Technology

    2003-11-01

    information management infrastructure, and the Semantic Web. We examine several facets of information management that will benefit from the Semantic Web as well as identify issues addressed by information management that will need to be addressed for mission-critical application of the Semantic Web. Finally, this paper discusses fundamental differences between the JBI and the Semantic Web that emanate from their current application contexts. We conclude with an overall perspective on their relationship and highlight areas of

  5. Semantic Web Research Trends and Directions

    DTIC Science & Technology

    2006-01-01

    social trust on the semantic web that builds upon the previous work to create end user applications that benefit from the semantic foundation. 2 Swoop...security, authentication, and privacy. However, the social component of trust is one that is both important and ideally suited for the Semantic Web. When the...Semantic Web-based social networks are augmented with trust information, it is possible to make computations over the values, and integrate the

  6. A call for collaborative semantics harmonization.

    PubMed

    Weng, Chunhua; Fridsma, Douglas B

    2006-01-01

    Semantic interoperability problems occur when shared vocabularies are used for inconsistent meanings in varied contexts among distributed and heterogeneous healthcare information systems. Methods such as XML only work at the syntax level and do not deal with semantics. We are missing the tools that support consensus semantics modeling across multidisciplinary healthcare communities. Here we briefly define what collaborative semantic harmonization is and describe an early design to achieve it.

  7. Retrieval and Monitoring Processes during Visual Working Memory: An ERP Study of the Benefit of Visual Semantics.

    PubMed

    Orme, Elizabeth; Brown, Louise A; Riby, Leigh M

    2017-01-01

    In this study, we examined electrophysiological indices of episodic remembering whilst participants recalled novel shapes, with and without semantic content, within a visual working memory paradigm. The components of interest were the parietal episodic (PE; 400-800 ms) and late posterior negativity (LPN; 500-900 ms), as these have previously been identified as reliable markers of recollection and post-retrieval monitoring, respectively. Fifteen young adults completed a visual matrix patterns task, assessing memory for low and high semantic visual representations. Matrices with either low semantic or high semantic content (containing familiar visual forms) were briefly presented to participants for study (1500 ms), followed by a retention interval (6000 ms) and finally a same/different recognition phase. The event-related potentials of interest were tracked from the onset of the recognition test stimuli. Analyses revealed equivalent amplitude for the earlier PE effect for the processing of both low and high semantic stimulus types. However, the LPN was more negative-going for the processing of the low semantic stimuli. These data are discussed in terms of relatively 'pure' and complete retrieval of high semantic items, where support can readily be recruited from semantic memory. However, for the low semantic items additional executive resources, as indexed by the LPN, are recruited when memory monitoring and uncertainty exist in order to recall previously studied items more effectively.

  8. Retrieval and Monitoring Processes during Visual Working Memory: An ERP Study of the Benefit of Visual Semantics

    PubMed Central

    Orme, Elizabeth; Brown, Louise A.; Riby, Leigh M.

    2017-01-01

    In this study, we examined electrophysiological indices of episodic remembering whilst participants recalled novel shapes, with and without semantic content, within a visual working memory paradigm. The components of interest were the parietal episodic (PE; 400–800 ms) and late posterior negativity (LPN; 500–900 ms), as these have previously been identified as reliable markers of recollection and post-retrieval monitoring, respectively. Fifteen young adults completed a visual matrix patterns task, assessing memory for low and high semantic visual representations. Matrices with either low semantic or high semantic content (containing familiar visual forms) were briefly presented to participants for study (1500 ms), followed by a retention interval (6000 ms) and finally a same/different recognition phase. The event-related potentials of interest were tracked from the onset of the recognition test stimuli. Analyses revealed equivalent amplitude for the earlier PE effect for the processing of both low and high semantic stimulus types. However, the LPN was more negative-going for the processing of the low semantic stimuli. These data are discussed in terms of relatively ‘pure’ and complete retrieval of high semantic items, where support can readily be recruited from semantic memory. However, for the low semantic items additional executive resources, as indexed by the LPN, are recruited when memory monitoring and uncertainty exist in order to recall previously studied items more effectively. PMID:28725203

  9. A Latent Trait Look at Pretest-Posttest Validation of Criterion-referenced Test Items.

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    1981-01-01

    Using criterion-referenced test item data collected in an empirical study, differences in item selection between Cox and Vargas' pretest-posttest validity index and a latent trait approach (evaluation of the item information function for the mastery score) are analyzed. (Author/GK)

  10. Group Comparisons in the Presence of Missing Data Using Latent Variable Modeling Techniques

    ERIC Educational Resources Information Center

    Raykov, Tenko; Marcoulides, George A.

    2010-01-01

    A latent variable modeling approach for examining population similarities and differences in observed variable relationship and mean indexes in incomplete data sets is discussed. The method is based on the full information maximum likelihood procedure of model fitting and parameter estimation. The procedure can be employed to test group identities…

  11. Behavioral Scale Reliability and Measurement Invariance Evaluation Using Latent Variable Modeling

    ERIC Educational Resources Information Center

    Raykov, Tenko

    2004-01-01

    A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described…

  12. Behavioral Scale Reliability and Measurement Invariance Evaluation Using Latent Variable Modeling

    ERIC Educational Resources Information Center

    Raykov, Tenko

    2004-01-01

    A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described…

  13. Group Comparisons in the Presence of Missing Data Using Latent Variable Modeling Techniques

    ERIC Educational Resources Information Center

    Raykov, Tenko; Marcoulides, George A.

    2010-01-01

    A latent variable modeling approach for examining population similarities and differences in observed variable relationship and mean indexes in incomplete data sets is discussed. The method is based on the full information maximum likelihood procedure of model fitting and parameter estimation. The procedure can be employed to test group identities…

  14. Semantic processing in information retrieval.

    PubMed Central

    Rindflesch, T. C.; Aronson, A. R.

    1993-01-01

    Intuition suggests that one way to enhance the information retrieval process would be the use of phrases to characterize the contents of text. A number of researchers, however, have noted that phrases alone do not improve retrieval effectiveness. In this paper we briefly review the use of phrases in information retrieval and then suggest extensions to this paradigm using semantic information. We claim that semantic processing, which can be viewed as expressing relations between the concepts represented by phrases, will in fact enhance retrieval effectiveness. The availability of the UMLS domain model, which we exploit extensively, significantly contributes to the feasibility of this processing. PMID:8130547

  15. Abstraction and natural language semantics.

    PubMed Central

    Kayser, Daniel

    2003-01-01

    According to the traditional view, a word prototypically denotes a class of objects sharing similar features, i.e. it results from an abstraction based on the detection of common properties in perceived entities. I explore here another idea: words result from abstraction of common premises in the rules governing our actions. I first argue that taking 'inference', instead of 'reference', as the basic issue in semantics does matter. I then discuss two phenomena that are, in my opinion, particularly difficult to analyse within the scope of traditional semantic theories: systematic polysemy and plurals. I conclude by a discussion of my approach, and by a summary of its main features. PMID:12903662

  16. Language networks in semantic dementia

    PubMed Central

    Agosta, Federica; Henry, Roland G.; Migliaccio, Raffaella; Neuhaus, John; Miller, Bruce L.; Dronkers, Nina F.; Brambati, Simona M.; Filippi, Massimo; Ogar, Jennifer M.; Wilson, Stephen M.

    2010-01-01

    Cognitive deficits in semantic dementia have been attributed to anterior temporal lobe grey matter damage; however, key aspects of the syndrome could be due to altered anatomical connectivity between language pathways involving the temporal lobe. The aim of this study was to investigate the left language-related cerebral pathways in semantic dementia using diffusion tensor imaging-based tractography and to combine the findings with cortical anatomical and functional magnetic resonance imaging data obtained during a reading activation task. The left inferior longitudinal fasciculus, arcuate fasciculus and fronto-parietal superior longitudinal fasciculus were tracked in five semantic dementia patients and eight healthy controls. The left uncinate fasciculus and the genu and splenium of the corpus callosum were also obtained for comparison with previous studies. From each tract, mean diffusivity, fractional anisotropy, as well as parallel and transverse diffusivities were obtained. Diffusion tensor imaging results were related to grey and white matter atrophy volume assessed by voxel-based morphometry and functional magnetic resonance imaging activations during a reading task. Semantic dementia patients had significantly higher mean diffusivity, parallel and transverse in the inferior longitudinal fasciculus. The arcuate and uncinate fasciculi demonstrated significantly higher mean diffusivity, parallel and transverse and significantly lower fractional anisotropy. The fronto-parietal superior longitudinal fasciculus was relatively spared, with a significant difference observed for transverse diffusivity and fractional anisotropy, only. In the corpus callosum, the genu showed lower fractional anisotropy compared with controls, while no difference was found in the splenium. The left parietal cortex did not show significant volume changes on voxel-based morphometry and demonstrated normal functional magnetic resonance imaging activation in response to reading items that

  17. Language networks in semantic dementia.

    PubMed

    Agosta, Federica; Henry, Roland G; Migliaccio, Raffaella; Neuhaus, John; Miller, Bruce L; Dronkers, Nina F; Brambati, Simona M; Filippi, Massimo; Ogar, Jennifer M; Wilson, Stephen M; Gorno-Tempini, Maria Luisa

    2010-01-01

    Cognitive deficits in semantic dementia have been attributed to anterior temporal lobe grey matter damage; however, key aspects of the syndrome could be due to altered anatomical connectivity between language pathways involving the temporal lobe. The aim of this study was to investigate the left language-related cerebral pathways in semantic dementia using diffusion tensor imaging-based tractography and to combine the findings with cortical anatomical and functional magnetic resonance imaging data obtained during a reading activation task. The left inferior longitudinal fasciculus, arcuate fasciculus and fronto-parietal superior longitudinal fasciculus were tracked in five semantic dementia patients and eight healthy controls. The left uncinate fasciculus and the genu and splenium of the corpus callosum were also obtained for comparison with previous studies. From each tract, mean diffusivity, fractional anisotropy, as well as parallel and transverse diffusivities were obtained. Diffusion tensor imaging results were related to grey and white matter atrophy volume assessed by voxel-based morphometry and functional magnetic resonance imaging activations during a reading task. Semantic dementia patients had significantly higher mean diffusivity, parallel and transverse in the inferior longitudinal fasciculus. The arcuate and uncinate fasciculi demonstrated significantly higher mean diffusivity, parallel and transverse and significantly lower fractional anisotropy. The fronto-parietal superior longitudinal fasciculus was relatively spared, with a significant difference observed for transverse diffusivity and fractional anisotropy, only. In the corpus callosum, the genu showed lower fractional anisotropy compared with controls, while no difference was found in the splenium. The left parietal cortex did not show significant volume changes on voxel-based morphometry and demonstrated normal functional magnetic resonance imaging activation in response to reading items that

  18. Bootstrapping to a Semantic Grid

    SciTech Connect

    Schwidder, Jens; Talbott, Tara; Myers, James D.

    2005-02-28

    The Scientific Annotation Middleware (SAM) is a set of components and services that enable researchers, applications, problem solving environments (PSE) and software agents to create metadata and annotations about data objects and document the semantic relationships between them. Developed starting in 2001, SAM allows applications to encode metadata within files or to manage metadata at the level of individual relationships as desired. SAM then provides mechanisms to expose metadata and relation¬ships encoded either way as WebDAV properties. In this paper, we report on work to further map this metadata into RDF and discuss the role of middleware such as SAM in bridging between traditional and semantic grid applications.

  19. Examining Lateralized Semantic Access Using Pictures

    ERIC Educational Resources Information Center

    Lovseth, Kyle; Atchley, Ruth Ann

    2010-01-01

    A divided visual field (DVF) experiment examined the semantic processing strategies employed by the cerebral hemispheres to determine if strategies observed with written word stimuli generalize to other media for communicating semantic information. We employed picture stimuli and vary the degree of semantic relatedness between the picture pairs.…

  20. Semantic Weight and Verb Retrieval in Aphasia

    ERIC Educational Resources Information Center

    Barde, Laura H. F.; Schwartz, Myrna F.; Boronat, Consuelo B.

    2006-01-01

    Individuals with agrammatic aphasia may have difficulty with verb production in comparison to nouns. Additionally, they may have greater difficulty producing verbs that have fewer semantic components (i.e., are semantically "light") compared to verbs that have greater semantic weight. A connectionist verb-production model proposed by Gordon and…

  1. Repetition Priming and Hyperpriming in Semantic Dementia

    ERIC Educational Resources Information Center

    Cumming, T. B.; Graham, K. S.; Patterson, K.

    2006-01-01

    Evidence from neurologically normal subjects suggests that repetition priming (RP) is independent of semantic processing. Therefore, we may expect patients with a selective deficit to conceptual knowledge to exhibit RP for words regardless of the integrity of their semantic representations. We tested six patients with semantic dementia (SD) on a…

  2. Examining Lateralized Semantic Access Using Pictures

    ERIC Educational Resources Information Center

    Lovseth, Kyle; Atchley, Ruth Ann

    2010-01-01

    A divided visual field (DVF) experiment examined the semantic processing strategies employed by the cerebral hemispheres to determine if strategies observed with written word stimuli generalize to other media for communicating semantic information. We employed picture stimuli and vary the degree of semantic relatedness between the picture pairs.…

  3. Semantic Relatedness for Evaluation of Course Equivalencies

    ERIC Educational Resources Information Center

    Yang, Beibei

    2012-01-01

    Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the…

  4. Semantic Weight and Verb Retrieval in Aphasia

    ERIC Educational Resources Information Center

    Barde, Laura H. F.; Schwartz, Myrna F.; Boronat, Consuelo B.

    2006-01-01

    Individuals with agrammatic aphasia may have difficulty with verb production in comparison to nouns. Additionally, they may have greater difficulty producing verbs that have fewer semantic components (i.e., are semantically "light") compared to verbs that have greater semantic weight. A connectionist verb-production model proposed by Gordon and…

  5. Semantic Relatedness for Evaluation of Course Equivalencies

    ERIC Educational Resources Information Center

    Yang, Beibei

    2012-01-01

    Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the…

  6. Metasemantics: On the Limits of Semantic Theory

    ERIC Educational Resources Information Center

    Parent, T.

    2009-01-01

    METASEMANTICS is a wake-up call for semantic theory: It reveals that some semantic questions have no adequate answer. (This is meant to be the "epistemic" point that certain semantic questions cannot be "settled"--not a metaphysical point about whether there is a fact-of-the-matter.) METASEMANTICS thus checks our default "optimism" that any…

  7. Metasemantics: On the Limits of Semantic Theory

    ERIC Educational Resources Information Center

    Parent, T.

    2009-01-01

    METASEMANTICS is a wake-up call for semantic theory: It reveals that some semantic questions have no adequate answer. (This is meant to be the "epistemic" point that certain semantic questions cannot be "settled"--not a metaphysical point about whether there is a fact-of-the-matter.) METASEMANTICS thus checks our default "optimism" that any…

  8. Chinese Character Decoding: A Semantic Bias?

    ERIC Educational Resources Information Center

    Williams, Clay; Bever, Thomas

    2010-01-01

    The effects of semantic and phonetic radicals on Chinese character decoding were examined. Our results suggest that semantic and phonetic radicals are each available for access when a corresponding task emphasizes one or the other kind of radical. But in a more neutral lexical recognition task, the semantic radical is more informative. Semantic…

  9. Chinese Character Decoding: A Semantic Bias?

    ERIC Educational Resources Information Center

    Williams, Clay; Bever, Thomas

    2010-01-01

    The effects of semantic and phonetic radicals on Chinese character decoding were examined. Our results suggest that semantic and phonetic radicals are each available for access when a corresponding task emphasizes one or the other kind of radical. But in a more neutral lexical recognition task, the semantic radical is more informative. Semantic…

  10. Scene Classfication Based on the Semantic-Feature Fusion Fully Sparse Topic Model for High Spatial Resolution Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Zhu, Qiqi; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features - the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

  11. Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases.

    PubMed

    Kobayashi, Norio; Ishii, Manabu; Takahashi, Satoshi; Mochizuki, Yoshiki; Matsushima, Akihiro; Toyoda, Tetsuro

    2011-07-01

    Global cloud frameworks for bioinformatics research databases become huge and heterogeneous; solutions face various diametric challenges comprising cross-integration, retrieval, security and openness. To address this, as of March 2011 organizations including RIKEN published 192 mammalian, plant and protein life sciences databases having 8.2 million data records, integrated as Linked Open or Private Data (LOD/LPD) using SciNetS.org, the Scientists' Networking System. The huge quantity of linked data this database integration framework covers is based on the Semantic Web, where researchers collaborate by managing metadata across public and private databases in a secured data space. This outstripped the data query capacity of existing interface tools like SPARQL. Actual research also requires specialized tools for data analysis using raw original data. To solve these challenges, in December 2009 we developed the lightweight Semantic-JSON interface to access each fragment of linked and raw life sciences data securely under the control of programming languages popularly used by bioinformaticians such as Perl and Ruby. Researchers successfully used the interface across 28 million semantic relationships for biological applications including genome design, sequence processing, inference over phenotype databases, full-text search indexing and human-readable contents like ontology and LOD tree viewers. Semantic-JSON services of SciNetS.org are provided at http://semanticjson.org.

  12. Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases

    PubMed Central

    Kobayashi, Norio; Ishii, Manabu; Takahashi, Satoshi; Mochizuki, Yoshiki; Matsushima, Akihiro; Toyoda, Tetsuro

    2011-01-01

    Global cloud frameworks for bioinformatics research databases become huge and heterogeneous; solutions face various diametric challenges comprising cross-integration, retrieval, security and openness. To address this, as of March 2011 organizations including RIKEN published 192 mammalian, plant and protein life sciences databases having 8.2 million data records, integrated as Linked Open or Private Data (LOD/LPD) using SciNetS.org, the Scientists' Networking System. The huge quantity of linked data this database integration framework covers is based on the Semantic Web, where researchers collaborate by managing metadata across public and private databases in a secured data space. This outstripped the data query capacity of existing interface tools like SPARQL. Actual research also requires specialized tools for data analysis using raw original data. To solve these challenges, in December 2009 we developed the lightweight Semantic-JSON interface to access each fragment of linked and raw life sciences data securely under the control of programming languages popularly used by bioinformaticians such as Perl and Ruby. Researchers successfully used the interface across 28 million semantic relationships for biological applications including genome design, sequence processing, inference over phenotype databases, full-text search indexing and human-readable contents like ontology and LOD tree viewers. Semantic-JSON services of SciNetS.org are provided at http://semanticjson.org. PMID:21632604

  13. A Methodology for Simulating Net-Centric Technologies: An Operations Research Approach

    DTIC Science & Technology

    2007-06-01

    Keywords: Captured enemy documents, latent semantic text analysis, network centric warfare, network science, language instant screening tool...document in any language (currently being developed in Arabic), index it for keywords through advanced latent semantic text analysis, append...metadata (GPS, date-time, keywords, unit), and relay the information to higher in near real time. Latent semantic text

  14. Phrase Based Topic Modeling for Semantic Information Processing in Biomedicine

    PubMed Central

    Yu, Zhiguo; Johnson, Todd R

    2017-01-01

    Given that unstructured data is increasing exponentially everyday, extracting and understanding the information, themes, and relationships from large collections of documents is increasingly important to researchers in many disciplines including biomedicine. Latent Dirichlet Allocation (LDA) is an unsupervised topic modeling technique based on the “bag-of-words” assumption that has been applied extensively to unveil hidden semantic themes within large sets of textual documents. Recently, it was extended using the “bag-of-n-grams” paradigm to account for word order. In this paper, we present an alternative phrase based LDA model to move from a bag of words or n-grams paradigm to a “bag-of-key-phrases” setting by applying a key phrase extraction technique, the C-value method, to further explore latent themes. We evaluate our approach by using a phrase intrusion user study and demonstrate that our model can help LDA generate better and more interpretable topics than those generated using the bag-of-n-grams approach. Given topic models essentially are statistical tools, an important problem in topic modeling is that of visualizing and interacting with the models to understand and extract new information from a collection. To evaluate our phrase based modeling approach in this context, we incorporate it in an open source interactive topic browser. Qualitative evaluations of this browser with biomedical experts demonstrate that our approach can aid biomedical researchers gain better and faster understanding of their document collections. PMID:28736774

  15. Orientation field estimation for latent fingerprint enhancement.

    PubMed

    Feng, Jianjiang; Zhou, Jie; Jain, Anil K

    2013-04-01

    Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint orientation field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.

  16. An assessment of the semantic network in patients with Alzheimer's disease.

    PubMed

    Chan, A S; Butters, N; Paulsen, J S; Salmon, D P; Swenson, M R; Maloney, L T

    1993-01-01

    Abstract The present study employed multidimensional scaling and ADDTREE clustering analyses to derive the cognitive maps and clustering representations of normal elderly controls (NC), patients with Alzheimer's disease (AD), and patients with Hun-tington's disease (HD); the analyses were performed on subjects' responses in a category fluency task that involved generating animal names for 60 sec. A measure of the proximity of animal names was used as an index of associational strength; MDS and ADDTREE estimates were based on this measure. A comparison of the NC, AD, and HD subjects' cognitive maps suggests that the semantic network of AD patients is abnormal in two ways. First, the organization of the semantic network is disrupted. Second, new abnormal associations and clusterings are formed. These results support the notion that AD is characterized by a breakdown in the structure of semantic knowledge and not primarily by a deficiency in the accessibility of semantic information.

  17. Action representation: crosstalk between semantics and pragmatics.

    PubMed

    Prinz, Wolfgang

    2014-03-01

    Marc Jeannerod pioneered a representational approach to movement and action. In his approach, motor representations provide both, declarative knowledge about action and procedural knowledge for action (action semantics and action pragmatics, respectively). Recent evidence from language comprehension and action simulation supports the claim that action pragmatics and action semantics draw on common representational resources, thus challenging the traditional divide between declarative and procedural action knowledge. To account for these observations, three kinds of theoretical frameworks are discussed: (i) semantics is grounded in pragmatics, (ii) pragmatics is anchored in semantics, and (iii) pragmatics is part and parcel of semantics. © 2013 Elsevier Ltd. All rights reserved.

  18. Semantic Annotation of Computational Components

    NASA Technical Reports Server (NTRS)

    Vanderbilt, Peter; Mehrotra, Piyush

    2004-01-01

    This paper describes a methodology to specify machine-processable semantic descriptions of computational components to enable them to be shared and reused. A particular focus of this scheme is to enable automatic compositon of such components into simple work-flows.

  19. Entanglement as a Semantic Resource

    NASA Astrophysics Data System (ADS)

    Dalla Chiara, Maria Luisa; Giuntini, Roberto; Ledda, Antonio; Leporini, Roberto; Sergioli, Giuseppe

    2010-10-01

    The characteristic holistic features of the quantum theoretic formalism and the intriguing notion of entanglement can be applied to a field that is far from microphysics: logical semantics. Quantum computational logics are new forms of quantum logic that have been suggested by the theory of quantum logical gates in quantum computation. In the standard semantics of these logics, sentences denote quantum information quantities: systems of qubits ( quregisters) or, more generally, mixtures of quregisters ( qumixes), while logical connectives are interpreted as special quantum logical gates (which have a characteristic reversible and dynamic behavior). In this framework, states of knowledge may be entangled, in such a way that our information about the whole determines our information about the parts; and the procedure cannot be, generally, inverted. In spite of its appealing properties, the standard version of the quantum computational semantics is strongly “Hilbert-space dependent”. This certainly represents a shortcoming for all applications, where real and complex numbers do not generally play any significant role (as happens, for instance, in the case of natural and of artistic languages). We propose an abstract version of quantum computational semantics, where abstract qumixes, quregisters and registers are identified with some special objects (not necessarily living in a Hilbert space), while gates are reversible functions that transform qumixes into qumixes. In this framework, one can give an abstract definition of the notions of superposition and of entangled pieces of information, quite independently of any numerical values. We investigate three different forms of abstract holistic quantum computational logic.

  20. Semantic Activation in Action Planning

    ERIC Educational Resources Information Center

    Lindemann, Oliver; Stenneken, Prisca; van Schie, Hein T.; Bekkering, Harold

    2006-01-01

    Four experiments investigated activation of semantic information in action preparation. Participants either prepared to grasp and use an object (e.g., to drink from a cup) or to lift a finger in association with the object's position following a go/no-go lexical-decision task. Word stimuli were consistent to the action goals of the object use…

  1. Generative Semantics and Dialect Geography.

    ERIC Educational Resources Information Center

    Ney, James W.

    An extrinsic relationship between generative semantics and dialect geography should be exploited because contemporary transformational grammarians have too easily ignored the work of the dialectologist and have been too readily satisfied with what might be called armchair evidence. The work of the dialect geographers needs to be taken into…

  2. 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…

  3. Colourful Semantics: A Clinical Investigation

    ERIC Educational Resources Information Center

    Bolderson, Sarah; Dosanjh, Christine; Milligan, Claudine; Pring, Tim; Chiat, Shula

    2011-01-01

    Children with language difficulties often omit verbs and grammatical elements and fail to complete sentences. Bryan (1997) described "colourful semantics", a therapy she used to treat a 5-year-old boy. The therapy uses colour coding to highlight the predicate argument structure of sentences. This study further tested the therapy's…

  4. A Note on Semantic Selection.

    ERIC Educational Resources Information Center

    Endo, Yoshio

    1989-01-01

    The notions of categorical selection (c-selection) and semantic selection (s-selection) as outlined in recent research on generative grammar are discussed. The first section addresses the type of selectional constraint imposed on English small clauses (e.g., "John considers [Mary smart]"). In the second section, it is suggested that the constraint…

  5. The Semantic Web in Education

    ERIC Educational Resources Information Center

    Ohler, Jason

    2008-01-01

    The semantic web or Web 3.0 makes information more meaningful to people by making it more understandable to machines. In this article, the author examines the implications of Web 3.0 for education. The author considers three areas of impact: knowledge construction, personal learning network maintenance, and personal educational administration.…

  6. Semantic Borders and Incomplete Understanding.

    PubMed

    Silva-Filho, Waldomiro J; Dazzani, Maria Virgínia

    2016-03-01

    In this article, we explore a fundamental issue of Cultural Psychology, that is our "capacity to make meaning", by investigating a thesis from contemporary philosophical semantics, namely, that there is a decisive relationship between language and rationality. Many philosophers think that for a person to be described as a rational agent he must understand the semantic content and meaning of the words he uses to express his intentional mental states, e.g., his beliefs and thoughts. Our argument seeks to investigate the thesis developed by Tyler Burge, according to which our mastery or understanding of the semantic content of the terms which form our beliefs and thoughts is an "incomplete understanding". To do this, we discuss, on the one hand, the general lines of anti-individualism or semantic externalism and, on the other, criticisms of the Burgean notion of incomplete understanding - one radical and the other moderate. We defend our understanding that the content of our beliefs must be described in the light of the limits and natural contingencies of our cognitive capacities and the normative nature of our rationality. At heart, anti-individualism leads us to think about the fact that we are social creatures, living in contingent situations, with important, but limited, cognitive capacities, and that we receive the main, and most important, portion of our knowledge simply from what others tell us. Finally, we conclude that this discussion may contribute to the current debate about the notion of borders.

  7. Incrementally Dissociating Syntax and Semantics

    ERIC Educational Resources Information Center

    Brennan, Jonathan R.

    2010-01-01

    A basic challenge for research into the neurobiology of language is understanding how the brain combines words to make complex representations. Linguistic theory divides this task into several computations including syntactic structure building and semantic composition. The close relationship between these computations, however, poses a strong…

  8. Incrementally Dissociating Syntax and Semantics

    ERIC Educational Resources Information Center

    Brennan, Jonathan R.

    2010-01-01

    A basic challenge for research into the neurobiology of language is understanding how the brain combines words to make complex representations. Linguistic theory divides this task into several computations including syntactic structure building and semantic composition. The close relationship between these computations, however, poses a strong…

  9. The Semantic Web in Education

    ERIC Educational Resources Information Center

    Ohler, Jason

    2008-01-01

    The semantic web or Web 3.0 makes information more meaningful to people by making it more understandable to machines. In this article, the author examines the implications of Web 3.0 for education. The author considers three areas of impact: knowledge construction, personal learning network maintenance, and personal educational administration.…

  10. Taxonomic and Thematic Semantic Systems.

    PubMed

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

    2017-03-23

    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 2 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 2 principles that may drive the development of distinct taxonomic and thematic semantic systems: differences between which features determine taxonomic versus thematic relations, and differences in the processing required to extract taxonomic versus 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. (PsycINFO Database Record

  11. Genres, Semantics, and Classroom Education.

    ERIC Educational Resources Information Center

    Lemke, Jay

    1988-01-01

    Argues that competence in academic subjects depends on mastery of their specialized patterns of language use. These patterns are described in terms of: 1) the semantics underlying Halliday's functional linguistics and 2) the structural analysis of communication genres. A sample classroom episode illustrates relationships among semantic…

  12. Semantic Fission through Dialect Fusion.

    ERIC Educational Resources Information Center

    Linn, Michael D.

    The linguistic atlas projects have provided much information on the regional distribution of pronunciation, vocabulary, and syntax and have given important evidence for a greater understanding of problems involved in semantic change, particularly in pointing out transition areas where dialects become fused. In a study supplementary to that…

  13. Implicit access to semantic information.

    PubMed

    Young, A W; Newcombe, F; Hellawell, D; De Haan, E

    1989-11-01

    Three experiments investigating the patient M.S.'s semantic memory are reported. Experiments 1 and 2 involved a category-membership decision task, in which M.S. was asked to determine whether a noun was a member of a specified semantic category. His performance in Experiment 1 was impaired for nouns from living categories in comparison with nouns from nonliving categories, and this impairment was especially marked for nouns of low typicality. Experiment 2 demonstrated an equivalent pattern of very poor performance to nouns of low familiarity from living categories. In Experiment 3 the effect of a category label on lexical decision was examined, using category labels as primes preceding nouns or pronounceable nonwords. Facilitation from related category label primes was found for typical and untypical members of living and nonliving semantic categories. These findings demonstrate that M.S. has impaired knowledge of the structure of living semantic categories when explicit access to this information is required (Experiments 1 and 2), but that some form of preserved category structure can be demonstrated in tasks which assess this implicitly (Experiment 3).

  14. Semantic search during divergent thinking.

    PubMed

    Hass, Richard W

    2017-09-01

    Divergent thinking, as a method of examining creative cognition, has not been adequately analyzed in the context of modern cognitive theories. This article casts divergent thinking responding in the context of theories of memory search. First, it was argued that divergent thinking tasks are similar to semantic fluency tasks, but are more constrained, and less well structured. Next, response time distributions from 54 participants were analyzed for temporal and semantic clustering. Participants responded to two prompts from the alternative uses test: uses for a brick and uses for a bottle, for two minutes each. Participants' cumulative response curves were negatively accelerating, in line with theories of search of associative memory. However, results of analyses of semantic and temporal clustering suggested that clustering is less evident in alternative uses responding compared to semantic fluency tasks. This suggests either that divergent thinking responding does not involve an exhaustive search through a clustered memory trace, but rather that the process is more exploratory, yielding fewer overall responses that tend to drift away from close associates of the divergent thinking prompt. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Recent advances in testing for latent TB.

    PubMed

    Schluger, Neil W; Burzynski, Joseph

    2010-12-01

    After more than a century of relying on skin testing for the diagnosis of latent TB infection, clinicians now have access to blood-based diagnostics in the form of interferon γ release assays (IGRAs). These tests are generally associated with higher sensitivity and specificity for diagnosis of latent TB infection. This article reviews the indications for testing and treatment of latent TB infection in the overall context of a TB control program and describes how IGRAs might be used in specific clinical settings and populations, including people having close contact with an active case of TB, the foreign born, and health-care workers.

  16. Extraction of latent images from printed media

    NASA Astrophysics Data System (ADS)

    Sergeyev, Vladislav; Fedoseev, Victor

    2015-12-01

    In this paper we propose an automatic technology for extraction of latent images from printed media such as documents, banknotes, financial securities, etc. This technology includes image processing by adaptively constructed Gabor filter bank for obtaining feature images, as well as subsequent stages of feature selection, grouping and multicomponent segmentation. The main advantage of the proposed technique is versatility: it allows to extract latent images made by different texture variations. Experimental results showing performance of the method over another known system for latent image extraction are given.

  17. An Efficient Web Page Ranking for Semantic Web

    NASA Astrophysics Data System (ADS)

    Chahal, P.; Singh, M.; Kumar, S.

    2014-01-01

    With the enormous amount of information presented on the web, the retrieval of relevant information has become a serious problem and is also the topic of research for last few years. The most common tools to retrieve information from web are search engines like Google. The Search engines are usually based on keyword searching and indexing of web pages. This approach is not very efficient as the result-set of web pages obtained include large irrelevant pages. Sometimes even the entire result-set may contain lot of irrelevant pages for the user. The next generation of search engines must address this problem. Recently, many semantic web search engines have been developed like Ontolook, Swoogle, which help in searching meaningful documents presented on semantic web. In this process the ranking of the retrieved web pages is very crucial. Some attempts have been made in ranking of semantic web pages but still the ranking of these semantic web documents is neither satisfactory and nor up to the user's expectations. In this paper we have proposed a semantic web based document ranking scheme that relies not only on the keywords but also on the conceptual instances present between the keywords. As a result only the relevant page will be on the top of the result-set of searched web pages. We explore all relevant relations between the keywords exploring the user's intention and then calculate the fraction of these relations on each web page to determine their relevance. We have found that this ranking technique gives better results than those by the prevailing methods.

  18. Event-related potential study of dynamic neural mechanisms of semantic organizational strategies in verbal learning.

    PubMed

    Blanchet, Sophie; Gagnon, Geneviève; Bastien, Célyne

    2007-09-19

    Neuroimaging and neuropsychological data indicate that the frontal regions are implicated in semantic organizational strategies in verbal learning. Whereas these approaches tend to adopt a localizationist view, we used event-related potentials (ERPs) to investigate the dynamic neural mechanisms involved in these strategies. We recorded ERPs using a 128-channel system in 12 young adults (23.75+/-3.02 years) during 3 encoding conditions that manipulated the levels of semantic organization demands. In the Unrelated condition, the words to encode did not share any semantic attributes. For both Spontaneous and Guided conditions, the words in each list were drawn from four semantic categories. In the Spontaneous condition, participants were not informed about the semantic relationship between items. In contrast, in the Guided condition, participants were instructed to improve their subsequent recall by mentally regrouping related items with the aid of category labels. Results indicated that the P200 amplitude increased with the greater organizational demand of semantic strategies. In contrast, the late positive component (LPC) amplitude was larger in both encoding conditions with semantic related words regardless of their instructions as compared to the Unrelated condition. Finally, there was greater right frontal sustained activity in the Spontaneous condition than in the Unrelated condition. Thus, our data indicate that the P200 is sensitive to attentional processes that increase with the organizational semantic demand. The LPC indexes associative processes voluntarily involved in linking related items together. Finally, the right frontal region appears to play an important role in the self-initiation of semantic organizational strategies.

  19. Lexical Semantics and Irregular Inflection

    PubMed Central

    Huang, Yi Ting; Pinker, Steven

    2010-01-01

    Whether a word has an irregular inflection does not depend on its sound alone: compare lie-lay (recline) and lie-lied (prevaricate). Theories of morphology, particularly connectionist and symbolic models, disagree on which nonphonological factors are responsible. We test four possibilities: (1) Lexical effects, in which two lemmas differ in whether they specify an irregular form; (2) Semantic effects, in which the semantic features of a word become associated with regular or irregular forms; (3) Morphological structure effects, in which a word with a headless structure (e.g., a verb derived from a noun) blocks access to a stored irregular form; (4) Compositionality effects, in which the stored combination of an irregular word’s meaning (e.g., the verb’s inherent aspect) with the meaning of the inflection (e.g., pastness) doesn’t readily transfer to new senses with different combinations of such meanings. In four experiments, speakers were presented with existing and novel verbs and asked to rate their past-tense forms, semantic similarities, grammatical structure, and aspectual similarities. We found (1) an interaction between semantic and phonological similarity, coinciding with reported strategies of analogizing to known verbs and implicating lexical effects; (2) weak and inconsistent effects of semantic similarity; (3) robust effects of morphological structure, and (4) robust effects of aspectual compositionality. Results are consistent with theories of language that invoke lexical entries and morphological structure, and which differentiate the mode of storage of regular and irregular verbs. They also suggest how psycholinguistic processes have shaped vocabulary structure over history. PMID:21151703

  20. 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.

  1. 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.

  2. [Semantic memory training in Alzheimer's disease].

    PubMed

    Goudour, Amandine; Samson, Séverine; Bakchine, Serge; Ehrlé, Nathalie

    2011-06-01

    The purpose of this study was to assess the effects of semantic stimulation of Alzheimer's patients on semantic memory comparatively to psychological support. We conducted semantic training with two target categories (musical instruments and human actions), because these concepts were massively failed in previous data collected in Alzheimer's disease. Ten patients (57-78 year old, MMSE scores from 17 to 26) were divided in an experimental and a control group where patients received psychological support instead of semantic cognitive training. Semantic abilities were significantly improved in patients from the experimental group, but only after semantic stimulation involving examples from musical instrument category. However, further analysis failed to show an item-specific improvement, suggesting that our results could be explained by a general increase of semantic retrieval. Results could also be explained by some motivational effect caused by more attractive material. Implications for future research and clinical applications are discussed.

  3. When Chinese semantics meets failed syntax.

    PubMed

    Yu, Jing; Zhang, Yaxu

    2008-05-07

    Previous event-related potential studies in Indo-European languages reported a surprising finding that failed syntactic category processing appears to block lexical-semantic integration, suggesting a functional primacy of syntax over semantics. An event-related potential experiment was conducted to test whether there is such primacy in Chinese sentence reading, using sentences containing either semantic only violations, combined syntactic category and semantic violations, or no violations. Semantic only violations elicited a centro-parietal negativity and combined violations a broadly distributed, but centro-parietally focused negativity, both in the 300-500 ms window and followed by a P600, suggesting that semantic integration proceeds even when syntactic category processing fails. Thus, there is no functional primacy of syntactic category over semantic processes during Chinese sentence reading.

  4. Category-specific semantic deficits in Alzheimer's disease: a semantic priming study.

    PubMed

    Hernández, Mireia; Costa, Albert; Juncadella, Montserrat; Sebastián-Gallés, Núria; Reñé, Ramón

    2008-03-07

    Category-specific semantic deficits in individuals suffering brain damage after relatively focal lesions provide an important source of evidence about the organization of semantic knowledge. However, whether Alzheimer's disease (AD), in which the brain damage is more widespread, affects semantic categories to a different extent is still controversial. In the present study, we assess this issue by means of the semantic priming technique. AD patients with a mild impairment of their semantic knowledge showed comparable priming effects to that of controls for the categories of animals and artifacts. Interestingly, however, patients with a moderate impairment of their semantic knowledge showed a normal priming effect for animals but a very reduced priming effect (if any) for artifacts. These results reveal that AD may affect the semantic knowledge of different semantic categories to a different extent. The implications of this observation for current theoretical accounts of semantic representation in the brain are discussed.

  5. Retrieved Latent Heating from TRMM

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Smith, Eric A.; Houze Jr, Robert

    2008-01-01

    The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for three-fourths of the total heat energy available to the Earth's atmosphere. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to meso-synoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations in environmental prediction models. The status of retrieved TRMM LH products, TRMM LH inter-comparison and validation project, current TRMM LH applications and critic issues/action items (based on previous five TRMM LH workshops) is presented in this article.

  6. Semantically linking and browsing PubMed abstracts with gene ontology

    PubMed Central

    Vanteru, Bhanu C; Shaik, Jahangheer S; Yeasin, Mohammed

    2008-01-01

    Background The technological advances in the past decade have lead to massive progress in the field of biotechnology. The documentation of the progress made exists in the form of research articles. The PubMed is the current most used repository for bio-literature. PubMed consists of about 17 million abstracts as of 2007 that require methods to efficiently retrieve and browse large volume of relevant information. The State-of-the-art technologies such as GOPubmed use simple keyword-based techniques for retrieving abstracts from the PubMed and linking them to the Gene Ontology (GO). This paper changes the paradigm by introducing semantics enabled technique to link the PubMed to the Gene Ontology, called, SEGOPubmed for ontology-based browsing. Latent Semantic Analysis (LSA) framework is used to semantically interface PubMed abstracts to the Gene Ontology. Results The Empirical analysis is performed to compare the performance of the SEGOPubmed with the GOPubmed. The analysis is initially performed using a few well-referenced query words. Further, statistical analysis is performed using GO curated dataset as ground truth. The analysis suggests that the SEGOPubmed performs better than the classic GOPubmed as it incorporates semantics. Conclusions The LSA technique is applied on the PubMed abstracts obtained based on the user query and the semantic similarity between the query and the abstracts. The analyses using well-referenced keywords show that the proposed semantic-sensitive technique outperformed the string comparison based techniques in associating the relevant abstracts to the GO terms. The SEGOPubmed also extracted the abstracts in which the keywords do not appear in isolation (i.e. they appear in combination with other terms) that could not be retrieved by simple term matching techniques. PMID:18366599

  7. Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

    PubMed

    Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu

    2016-09-20

    In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate and fast retrieval method for breast histopathological image. Specifically, the method presents local statistical feature of nuclei for morphology and distribution of nuclei, and employs Gabor feature to describe texture information. Latent Dirichlet Allocation model is utilized for high-level semantic mining. Locality- Sensitive Hashing is used to speed up the search. Experiments on a WSI database with over 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, WSI archiving and management.

  8. On the Latent Variable Interpretation in Sum-Product Networks.

    PubMed

    Peharz, Robert; Gens, Robert; Pernkopf, Franz; Domingos, Pedro

    2016-11-18

    One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

  9. The effect of semantic categorisation on recall memory in amnesia.

    PubMed

    Channon, S; Daum, I

    2000-01-01

    Amnesic patients were compared to a healthy control group on recall of word lists containing semantically-related or unrelated words. As expected on the basis of previous literature, the amnesic group performed below the control group on all measures of recall. When total recall scores for each list were used as the index of performance, their scores were not significantly affected by the type of list, unlike those of the control group. Comparison of serial position effects for different parts of the lists revealed that the control group derived greater benefit from semantic relatedness in recall of items from the middle positions. This effect was not shown by the amnesic group, who showed similar U-shaped serial position curves for recall of all three lists, and appeared to use a more passive recall strategy than the control group. The findings are discussed in relation to our current understanding of amnesic deficits.

  10. Knowledge networks in the age of the Semantic Web.

    PubMed

    Neumann, Eric; Prusak, Larry

    2007-05-01

    The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.

  11. Synonym extraction and abbreviation expansion with ensembles of semantic spaces

    PubMed Central

    2014-01-01

    Background Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results A combination of two distributional models – Random Indexing and Random Permutation – employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora – a corpus of clinical text and a corpus of medical journal articles – further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions This study demonstrates

  12. Unsupervised Semantic Labeling Framework for Identification of Complex Facilities in High-resolution Remote Sensing Images

    SciTech Connect

    Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

    2010-01-01

    Nuclear proliferation is a major national security concern for many countries. 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 an unsupervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 70 images collected under different spatial and temporal settings over the globe representing two major semantic categories: nuclear and coal power plants. Initial experimental results show a reasonable discrimination of these two categories even though they share highly overlapping and common objects. This research also identified several research challenges associated with nuclear proliferation monitoring using high resolution remote sensing images.

  13. Reactivation of Latent Viruses in Space

    NASA Technical Reports Server (NTRS)

    Pierson, D. L.; Mehta, S. K.; Tyring, S. K.; Lugg, D. J.

    1999-01-01

    Reactivation of latent viruses is an important health risk for people working and living in physically isolated extreme environments such as Antarctica and space. Preflight quarantine does not significantly reduce the risk associated with latent viruses, however, pharmaceutical countermeasures are available for some viruses. The molecular basis of latency is not fully understood, but physical and psychosocial stresses are known to initiate the reactivation of latent viruses. Presumably, stress induced changes in selected hormones lead to alterations in the cell- mediated immune (CMI) response resulting in increased shedding of latent viruses. Limited access to space makes the use of ground-based analogs essential. The Australian Antarctic stations serve as a good stress model and simulate many aspects of space flight. Closed environmental chambers have been used to simulate space flight since the Skylab missions and have also proven to be a valuable analog of selected aspects of space flight.

  14. Reactivation of Latent Viruses in Space

    NASA Technical Reports Server (NTRS)

    Pierson, D. L.; Mehta, S. K.; Tyring, S. K.; Lugg, D. J.

    1999-01-01

    Reactivation of latent viruses is an important health risk for people working and living in physically isolated extreme environments such as Antarctica and space. Preflight quarantine does not significantly reduce the risk associated with latent viruses, however, pharmaceutical countermeasures are available for some viruses. The molecular basis of latency is not fully understood, but physical and psychosocial stresses are known to initiate the reactivation of latent viruses. Presumably, stress induced changes in selected hormones lead to alterations in the cell- mediated immune (CMI) response resulting in increased shedding of latent viruses. Limited access to space makes the use of ground-based analogs essential. The Australian Antarctic stations serve as a good stress model and simulate many aspects of space flight. Closed environmental chambers have been used to simulate space flight since the Skylab missions and have also proven to be a valuable analog of selected aspects of space flight.

  15. Automatic Evaluation for E-Learning Using Latent Semantic Analysis: A Use Case

    ERIC Educational Resources Information Center

    Farrus, Mireia; Costa-jussa, Marta R.

    2013-01-01

    Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de…

  16. Latent Semantic Analysis As a Tool for Learner Positioning in Learning Networks for Lifelong Learning

    ERIC Educational Resources Information Center

    van Bruggen, Jan; Sloep, Peter; van Rosmalen, Peter; Brouns, Francis; Vogten, Hubert; Koper, Rob; Tattersall, Colin

    2004-01-01

    As we move towards distributed, self-organised learning networks for lifelong learning to which multiple providers contribute content, there is a need to develop new techniques to determine where learners can be positioned in these networks. Positioning requires us to map characteristics of the learner onto characteristics of learning materials…

  17. Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension

    PubMed Central

    Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

    2016-01-01

    In two experiments, we attempted to replicate and extend findings by Günther et al. (2016) that word similarity measures obtained from distributional semantics models—Latent Semantic Analysis (LSA) and Hyperspace Analog to Language (HAL)—predict lexical priming effects. To this end, we used the pseudo-random method to generate item material while systematically controlling for word similarities introduced by Günther et al. (2016) which was based on LSA cosine similarities (Experiment 1) and HAL cosine similarities (Experiment 2). Extending the original study, we used semantic spaces created from far larger corpora, and implemented several additional methodological improvements. In Experiment 1, we only found a significant effect of HAL cosines on lexical decision times, while we found significant effects for both LSA and HAL cosines in Experiment 2. As further supported by an analysis of the pooled data from both experiments, this indicates that HAL cosines are a better predictor of priming effects than LSA cosines. Taken together, the results replicate the finding that priming effects can be predicted from distributional semantic similarity measures. PMID:27822195

  18. Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension.

    PubMed

    Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

    2016-01-01

    In two experiments, we attempted to replicate and extend findings by Günther et al. (2016) that word similarity measures obtained from distributional semantics models-Latent Semantic Analysis (LSA) and Hyperspace Analog to Language (HAL)-predict lexical priming effects. To this end, we used the pseudo-random method to generate item material while systematically controlling for word similarities introduced by Günther et al. (2016) which was based on LSA cosine similarities (Experiment 1) and HAL cosine similarities (Experiment 2). Extending the original study, we used semantic spaces created from far larger corpora, and implemented several additional methodological improvements. In Experiment 1, we only found a significant effect of HAL cosines on lexical decision times, while we found significant effects for both LSA and HAL cosines in Experiment 2. As further supported by an analysis of the pooled data from both experiments, this indicates that HAL cosines are a better predictor of priming effects than LSA cosines. Taken together, the results replicate the finding that priming effects can be predicted from distributional semantic similarity measures.

  19. Indexing Images.

    ERIC Educational Resources Information Center

    Rasmussen, Edie M.

    1997-01-01

    Focuses on access to digital image collections by means of manual and automatic indexing. Contains six sections: (1) Studies of Image Systems and their Use; (2) Approaches to Indexing Images; (3) Image Attributes; (4) Concept-Based Indexing; (5) Content-Based Indexing; and (6) Browsing in Image Retrieval. Contains 105 references. (AEF)

  20. The Aging Semantic Differential in Mandarin Chinese: Measuring Attitudes toward Older Adults in China.

    PubMed

    Gonzales, Ernest; Marchiondo, Lisa A; Tan, Jing; Wang, Yi; Chen, Huajuan

    2017-02-16

    The Aging Semantic Differential (ASD) is the most widely used instrument to measure young people's attitudes towards older adults. This study translated the ASD to Mandarin and examined its psychometric properties. The Mandarin-ASD contains three latent factors (Personality and Mental Health, Societal Participation, and Physical) that have high internal reliability and reasonable discriminate validity. Social work researchers, practitioners and allied professionals may utilize the ASD-Mandarin instrument to measure young people's attitudes towards older adults in China. We issue a call for a universal-ASD that can be applied across different cultural contexts.

  1. Latent phenotypes pervade gene regulatory circuits

    PubMed Central

    2014-01-01

    Background Latent phenotypes are non-adaptive byproducts of adaptive phenotypes. They exist in biological systems as different as promiscuous enzymes and genome-scale metabolic reaction networks, and can give rise to evolutionary adaptations and innovations. We know little about their prevalence in the gene expression phenotypes of regulatory circuits, important sources of evolutionary innovations. Results Here, we study a space of more than sixteen million three-gene model regulatory circuits, where each circuit is represented by a genotype, and has one or more functions embodied in one or more gene expression phenotypes. We find that the majority of circuits with single functions have latent expression phenotypes. Moreover, the set of circuits with a given spectrum of functions has a repertoire of latent phenotypes that is much larger than that of any one circuit. Most of this latent repertoire can be easily accessed through a series of small genetic changes that preserve a circuit’s main functions. Both circuits and gene expression phenotypes that are robust to genetic change are associated with a greater number of latent phenotypes. Conclusions Our observations suggest that latent phenotypes are pervasive in regulatory circuits, and may thus be an important source of evolutionary adaptations and innovations involving gene regulation. PMID:24884746

  2. Latent phenotypes pervade gene regulatory circuits.

    PubMed

    Payne, Joshua L; Wagner, Andreas

    2014-05-30

    Latent phenotypes are non-adaptive byproducts of adaptive phenotypes. They exist in biological systems as different as promiscuous enzymes and genome-scale metabolic reaction networks, and can give rise to evolutionary adaptations and innovations. We know little about their prevalence in the gene expression phenotypes of regulatory circuits, important sources of evolutionary innovations. Here, we study a space of more than sixteen million three-gene model regulatory circuits, where each circuit is represented by a genotype, and has one or more functions embodied in one or more gene expression phenotypes. We find that the majority of circuits with single functions have latent expression phenotypes. Moreover, the set of circuits with a given spectrum of functions has a repertoire of latent phenotypes that is much larger than that of any one circuit. Most of this latent repertoire can be easily accessed through a series of small genetic changes that preserve a circuit's main functions. Both circuits and gene expression phenotypes that are robust to genetic change are associated with a greater number of latent phenotypes. Our observations suggest that latent phenotypes are pervasive in regulatory circuits, and may thus be an important source of evolutionary adaptations and innovations involving gene regulation.

  3. Unsupervised mining of frequent tags for clinical eligibility text indexing.

    PubMed

    Miotto, Riccardo; Weng, Chunhua

    2013-12-01

    Clinical text, such as clinical trial eligibility criteria, is largely underused in state-of-the-art medical search engines due to difficulties of accurate parsing. This paper proposes a novel methodology to derive a semantic index for clinical eligibility documents based on a controlled vocabulary of frequent tags, which are automatically mined from the text. We applied this method to eligibility criteria on ClinicalTrials.gov and report that frequent tags (1) define an effective and efficient index of clinical trials and (2) are unlikely to grow radically when the repository increases. We proposed to apply the semantic index to filter clinical trial search results and we concluded that frequent tags reduce the result space more efficiently than an uncontrolled set of UMLS concepts. Overall, unsupervised mining of frequent tags from clinical text leads to an effective semantic index for the clinical eligibility documents and promotes their computational reuse. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Inquiry Semantics: A Functional Semantics of Natural Language Grammar

    DTIC Science & Technology

    1983-10-01

    full detail and then execute such plans. In fact, such a mode of operation has serious difficulties , and so it is worthwhile to consider other...Association for Computational Linguitics ,.held in Pia, Italy, In September 1983. I=. ....._ _. . . ... .._i__ _ _I 2 INQUIRY SEMANTICS With imposed... difficulties which do not arise from difficulties of representation. For example, knowing what to thematize and what to mark, knowing causes and

  5. Exploiting semantic linkages among multiple sources for semantic information retrieval

    NASA Astrophysics Data System (ADS)

    Li, JianQiang; Yang, Ji-Jiang; Liu, Chunchen; Zhao, Yu; Liu, Bo; Shi, Yuliang

    2014-07-01

    The vision of the Semantic Web is to build a global Web of machine-readable data to be consumed by intelligent applications. As the first step to make this vision come true, the initiative of linked open data has fostered many novel applications aimed at improving data accessibility in the public Web. Comparably, the enterprise environment is so different from the public Web that most potentially usable business information originates in an unstructured form (typically in free text), which poses a challenge for the adoption of semantic technologies in the enterprise environment. Considering that the business information in a company is highly specific and centred around a set of commonly used concepts, this paper describes a pilot study to migrate the concept of linked data into the development of a domain-specific application, i.e. the vehicle repair support system. The set of commonly used concepts, including the part name of a car and the phenomenon term on the car repairing, are employed to build the linkage between data and documents distributed among different sources, leading to the fusion of documents and data across source boundaries. Then, we describe the approaches of semantic information retrieval to consume these linkages for value creation for companies. The experiments on two real-world data sets show that the proposed approaches outperform the best baseline 6.3-10.8% and 6.4-11.1% in terms of top five and top 10 precisions, respectively. We believe that our pilot study can serve as an important reference for the development of similar semantic applications in an enterprise environment.

  6. Continuous Time Dynamic Topic Models

    DTIC Science & Technology

    2008-06-20

    called topics, can be used to explain the observed collection. LDA is a probabilistic extension of latent semantic indexing (LSI) [5] and probabilistic... latent semantic indexing (pLSI) [11]. Owing to its formal generative semantics, LDA has been extended and applied to authorship [19], email [15...Steyvers. Probabilistic topic models. In Latent Semantic Analysis: A Road to Meaning. 2006. [9] T. L. Griffiths and M. Steyvers. Finding scientific

  7. Habituation, latent inhibition, and extinction.

    PubMed

    Jordan, Wesley P; Todd, Travis P; Bucci, David J; Leaton, Robert N

    2015-06-01

    In two conditioned suppression experiments with a latent inhibition (LI) design, we measured the habituation of rats in preexposure, their LI during conditioning, and then extinction over days. In the first experiment, lick suppression, the preexposed group (PE) showed a significant initial unconditioned response (UR) to the target stimulus and significant long-term habituation (LTH) of that response over days. The significant difference between the PE and nonpreexposed (NPE) groups on the first conditioning trial was due solely to the difference in their URs to the conditioned stimulus (CS)-a habituated response (PE) and an unhabituated response (NPE). In the second experiment, bar-press suppression, little UR to the target stimulus was apparent during preexposure, and no detectable LTH. Thus, there was no difference between the PE and NPE groups on the first conditioning trial. Whether the UR to the CS confounds the interpretation of LI (Exp. 1) or not (Exp. 2) can only be known if the UR is measured. In both experiments, LI was observed in acquisition. Also in both experiments, rats that were preexposed and then conditioned to asymptote were significantly more resistant to extinction than were the rats not preexposed. This result contrasts with the consistently reported finding that preexposure either produces less resistance to extinction or has no effect on extinction. The effect of stimulus preexposure survived conditioning to asymptote and was reflected directly in extinction. These two experiments provide a cautionary procedural note for LI experiments and have shown an unexpected extinction effect that may provide new insights into the interpretation of LI.

  8. Adopting Abstract Images for Semantic Scene Understanding.

    PubMed

    Zitnick, C Lawrence; Vedantam, Ramakrishna; Parikh, Devi

    2016-04-01

    Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. In this paper, we propose studying semantic information in abstract images created from collections of clip art. Abstract images provide several advantages over real images. They allow for the direct study of how to infer high-level semantic information, since they remove the reliance on noisy low-level object, attribute and relation detectors, or the tedious hand-labeling of real images. Importantly, abstract images also allow the ability to generate sets of semantically similar scenes. Finding analogous sets of real images that are semantically similar would be nearly impossible. We create 1,002 sets of 10 semantically similar abstract images with corresponding written descriptions. We thoroughly analyze this dataset to discover semantically important features, the relations of words to visual features and methods for measuring semantic similarity. Finally, we study the relation between the saliency and memorability of objects and their semantic importance.

  9. The evolution of semantic systems.

    PubMed

    Bainbridge, William Sims

    2004-05-01

    Semantic or cultural systems are sets of concepts connected by meaningful relationships, and they exhibit properties similar to those of populations of biological organisms. Drawing upon ideas from evolutionary biology and methods from information technology, this article explores the potential for research and engineering on the evolution of semantic systems. Such work in cultural genetics requires two things: (1) a rigorous but evolving taxonomic system to categorize cultural artifacts, elements, and clusters, and (2) a set of hypotheses about the processes that cause evolutionary change. This article illustrates systematic approaches to cultural taxonomy with data on the popular ideology of the space program, science fiction motion pictures, nanotechnology books, and nanotechnology research grants. It offers hypotheses derived from evolutionary and population biology that might be useful in explaining cultural evolution.

  10. 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.

  11. Semantic Event Correlation Using Ontologies

    NASA Astrophysics Data System (ADS)

    Moser, Thomas; Roth, Heinz; Rozsnyai, Szabolcs; Mordinyi, Richard; Biffl, Stefan

    Complex event processing (CEP) is a software architecture paradigm that aims at low latency, high throughput, and quick adaptability of applications for supporting and improving event-driven business processes. Events sensed in real time are the basic information units on which CEP applications operate and react in self-contained decision cycles based on defined processing logic and rules. Event correlation is necessary to relate events gathered from various sources for detecting patterns and situations of interest in the business context. Unfortunately, event correlation has been limited to syntactically identical attribute values instead of addressing semantically equivalent attribute meanings. Semantic equivalence is particularly relevant if events come from organizations that use different terminologies for common concepts.

  12. Semantic priming of familiar songs.

    PubMed

    Johnson, Sarah K; Halpern, Andrea R

    2012-05-01

    We explored the functional organization of semantic memory for music by comparing priming across familiar songs both within modalities (Experiment 1, tune to tune; Experiment 3, category label to lyrics) and across modalities (Experiment 2, category label to tune; Experiment 4, tune to lyrics). Participants judged whether or not the target tune or lyrics were real (akin to lexical decision tasks). We found significant priming, analogous to linguistic associative-priming effects, in reaction times for related primes as compared to unrelated primes, but primarily for within-modality comparisons. Reaction times to tunes (e.g., "Silent Night") were faster following related tunes ("Deck the Hall") than following unrelated tunes ("God Bless America"). However, a category label (e.g., Christmas) did not prime tunes from within that category. Lyrics were primed by a related category label, but not by a related tune. These results support the conceptual organization of music in semantic memory, but with potentially weaker associations across modalities.

  13. Imprint of the ENSO on rainfall and latent heating variability over the Southern South China Sea from TRMM observations

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Huang, Ke

    2016-04-01

    Analyses of the Tropical Rainfall Measuring Mission (TRMM) datasets revealed a prominent interannual variation in the convective-stratiform rainfall and latent heating over the southern South China Sea (SCS) during the winter monsoon between 1998 and 2010. Although the height of maximum latent heating remained nearly constant at around 7 km in all of the years, the year-to-year changes in the magnitudes of maximum latent heating over the region were noticeable. The interannual variations of the convective- stratiform rainfall and latent heating over the southern SCS were highly anti-correlated with the Niño-3 index, with more (less) rainfall and latent heating during La Niña (El Niño) years. Analysis of the large-scale environment revealed that years of active rainfall and latent heating corresponded to years of large deep convergence and relative humidity at 600 hPa. The moisture budget diagnosis indicated that the interannual variation of humidity at 600 hPa was largely modulated by the vertical moisture advection. The year-to-year changes in rainfall over the southern SCS were mainly caused by the interannual variations of the dynamic component associated with anomalous upward motions in the middle troposphere, while the interannual variations of the thermodynamic component associated with changes in surface specific humidity played a minor role. Larger latent heating over the southern SCS during La Niña years may possibly further enhance the local Hadley circulation over the SCS in the wintertime.

  14. Introspections on the Semantic Gap

    DTIC Science & Technology

    2015-04-14

    essential goal of virtual machine introspection ( VMI ) is security policy enforcement in the presence of an untrustworthy OS. One obstacle to this goal is...ABSTRACT Introspections on the Semantic Gap Report Title An essential goal of virtual machine introspection ( VMI ) is security policy enforcement in the...machine introspection ( VMI ) is security policy enforcement in the presence of an untrustworthy OS. One obstacle to this goal is the difficulty in

  15. Operational Reasoning and Denotational Semantics

    DTIC Science & Technology

    1975-08-01

    form is correctly computed by the interpreter. This is used to justify an inference rule - called ’LISP-induction* - which formalises induction on... peper contains example,? of the use of operational reasoning to prove properties of a denotationai semantics. By "operational reasoning" is meant...programs. This rule - called "LISP-induction" - is induction on the length of computations on the interpreter. Because the interpreter is correct LISP

  16. Aggressiveness as a latent personality trait of domestic dogs: Testing local independence and measurement invariance.

    PubMed

    Goold, Conor; Newberry, Ruth C

    2017-01-01

    Studies of animal personality attempt to uncover underlying or "latent" personality traits that explain broad patterns of behaviour, often by applying latent variable statistical models (e.g., factor analysis) to multivariate data sets. Two integral, but infrequently confirmed, assumptions of latent variable models in animal personality are: i) behavioural variables are independent (i.e., uncorrelated) conditional on the latent personality traits they reflect (local independence), and ii) personality traits are associated with behavioural variables in the same way across individuals or groups of individuals (measurement invariance). We tested these assumptions using observations of aggression in four age classes (4-10 months, 10 months-3 years, 3-6 years, over 6 years) of male and female shelter dogs (N = 4,743) in 11 different contexts. A structural equation model supported the hypothesis of two positively correlated personality traits underlying aggression across contexts: aggressiveness towards people and aggressiveness towards dogs (comparative fit index: 0.96; Tucker-Lewis index: 0.95; root mean square error of approximation: 0.03). Aggression across contexts was moderately repeatable (towards people: intraclass correlation coefficient (ICC) = 0.479; towards dogs: ICC = 0.303). However, certain contexts related to aggressiveness towards people (but not dogs) shared significant residual relationships unaccounted for by latent levels of aggressiveness. Furthermore, aggressiveness towards people and dogs in different contexts interacted with sex and age. Thus, sex and age differences in displays of aggression were not simple functions of underlying aggressiveness. Our results illustrate that the robustness of traits in latent variable models must be critically assessed before making conclusions about the effects of, or factors influencing, animal personality. Our findings are of concern because inaccurate "aggressive personality" trait attributions can be costly

  17. Entropy, semantic relatedness and proximity.

    PubMed

    Hahn, Lance W; Sivley, Robert M

    2011-09-01

    Although word co-occurrences within a document have been demonstrated to be semantically useful, word interactions over a local range have been largely neglected by psychologists due to practical challenges. Shannon's (Bell Systems Technical Journal, 27, 379-423, 623-665, 1948) conceptualization of information theory suggests that these interactions should be useful for understanding communication. Computational advances make an examination of local word-word interactions possible for a large text corpus. We used Brants and Franz's (2006) dataset to generate conditional probabilities for 62,474 word pairs and entropy calculations for 9,917 words in Nelson, McEvoy, and Schreiber's (Behavior Research Methods, Instruments, & Computers, 36, 402-407, 2004) free association norms. Semantic associativity correlated moderately with the probabilities and was stronger when the two words were not adjacent. The number of semantic associates for a word and the entropy of a word were also correlated. Finally, language entropy decreases from 11 bits for single words to 6 bits per word for four-word sequences. The probabilities and entropies discussed here are included in the supplemental materials for the article.

  18. Semantic priming in the prime task effect: evidence of automatic semantic processing of distractors.

    PubMed

    Marí-Beffa, P; Fuentes, L J; Catena, A; Houghton, G

    2000-06-01

    The automaticity of the semantic processing of words has been questioned because of the reduction of semantic priming when the prime word is processed nonsemantically--for example, in letter search (the prime task effect). In two experiments, prime distractor words produced semantic priming in a subsequent lexical decision task, but with the direction of priming (positive or negative) depending on the prime task. Lexico-semantic tasks produced negative semantic priming, whereas letter search produced positive semantic priming. These results are discussed in terms of task-based inhibition. We argue that, given the results from the distractors, the absence of semantic priming does not indicate an absence of semantic activation but reflects the action of control processes on prepotent responses when less practiced responses are needed.

  19. Integration of Sentence-Level Semantic Information in Parafovea: Evidence from the RSVP-Flanker Paradigm.

    PubMed

    Zhang, Wenjia; Li, Nan; Wang, Xiaoyue; Wang, Suiping

    2015-01-01

    During text reading, the parafoveal word was usually presented between 2° and 5° from the point of fixation. Whether semantic information of parafoveal words can be processed during sentence reading is a critical and long-standing issue. Recently, studies using the RSVP-flanker paradigm have shown that the incongruent parafoveal word, presented as right flanker, elicited a more negative N400 compared with the congruent parafoveal word. This suggests that the semantic information of parafoveal words can be extracted and integrated during sentence reading, because the N400 effect is a classical index of semantic integration. However, as most previous studies did not control the word-pair congruency of the parafoveal and the foveal words that were presented in the critical triad, it is still unclear whether such integration happened at the sentence level or just at the word-pair level. The present study addressed this question by manipulating verbs in Chinese sentences to yield either a semantically congruent or semantically incongruent context for the critical noun. In particular, the interval between the critical nouns and verbs was controlled to be 4 or 5 characters. Thus, to detect the incongruence of the parafoveal noun, participants had to integrate it with the global sentential context. The results revealed that the N400 time-locked to the critical triads was more negative in incongruent than in congruent sentences, suggesting that parafoveal semantic information can be integrated at the sentence level during Chinese reading.

  20. 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

  1. Electrophysiology reveals semantic priming at a short SOA irrespective of depth of prime processing.

    PubMed

    Küper, Kristina; Heil, Martin

    2009-04-03

    The otherwise robust behavioral semantic priming effect is reduced to the point of being absent when a letter search has to be performed on the prime word. As a result the automaticity of semantic activation has been called into question. It is unclear, however, in how far automatic processes are even measurable in the letter search priming paradigm as the prime task necessitates a long prime-probe stimulus-onset asynchrony (SOA). In a modified procedure, a short SOA can be realized by delaying the prime task response until after participants have made a lexical decision on the probe. While the absence of lexical decision priming has already been demonstrated in this design it seems premature to draw any definite conclusions from this purely behavioral result since event related potential (ERP) measures have been shown to be a more sensitive index of semantic activation. Using the modified paradigm we thus recorded ERP in addition to lexical decision times. Stimuli were presented at two different SOAs (240 ms vs. 840 ms) and participants performed either a grammatical discrimination (Experiment 1) or a letter search (Experiment 2) on the prime. Irrespective of prime task, the modulation of the N400, the ERP correlate of semantic activation, provided clear-cut evidence of semantic processing at the short SOA. Implications for theories of semantic activation as well as the constraints of the delayed prime task procedure are discussed.

  2. Latent Curve Models and Latent Change Score Models Estimated in R

    ERIC Educational Resources Information Center

    Ghisletta, Paolo; McArdle, John J.

    2012-01-01

    In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R…

  3. Latent Curve Models and Latent Change Score Models Estimated in R

    ERIC Educational Resources Information Center

    Ghisletta, Paolo; McArdle, John J.

    2012-01-01

    In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R…

  4. Latent-Trait Latent-Class Analysis of Self-Disclosure in the Work Environment

    ERIC Educational Resources Information Center

    Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk

    2005-01-01

    Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data…

  5. Quantifying Semantic Linguistic Maturity in Children.

    PubMed

    Hansson, Kristina; Bååth, Rasmus; Löhndorf, Simone; Sahlén, Birgitta; Sikström, Sverker

    2016-10-01

    We propose a method to quantify semantic linguistic maturity (SELMA) based on a high dimensional semantic representation of words created from the co-occurrence of words in a large text corpus. The method was applied to oral narratives from 108 children aged 4;0-12;10. By comparing the SELMA measure with maturity ratings made by human raters we found that SELMA predicted the rating of semantic maturity made by human raters over and above the prediction made using a child's age and number of words produced. We conclude that the semantic content of narratives changes in a predictable pattern with children's age and argue that SELMA is a measure quantifying semantic linguistic maturity. The study opens up the possibility of using quantitative measures for studying the development of semantic representation in children's narratives, and emphasizes the importance of word co-occurrences for understanding the development of meaning.

  6. An Electrophysiological Investigation of Semantic Incongruity Processing by People with Asperger's Syndrome

    ERIC Educational Resources Information Center

    Ring, Howard; Sharma, Simeran; Wheelwright, Sally; Barrett, Geoff

    2007-01-01

    The aim of this study was to investigate whether a physiological measure of impaired use of context could be obtained in people with Asperger's Syndrome (AS). The experimental paradigm employed was the use of electroencephalography to measure the detection of semantic incongruity within written sentences, as indexed by an N400 event-related…

  7. A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

    ERIC Educational Resources Information Center

    Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

    2011-01-01

    This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

  8. A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

    ERIC Educational Resources Information Center

    Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

    2011-01-01

    This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

  9. Brain Responses to Lexical-Semantic Priming in Children At-Risk for Dyslexia

    ERIC Educational Resources Information Center

    Torkildsen, Janne von Koss; Syversen, Gro; Simonsen, Hanne Gram; Moen, Inger; Lindgren, Magnus

    2007-01-01

    Deviances in early event-related potential (ERP) components reflecting auditory and phonological processing are well-documented in children at familial risk for dyslexia. However, little is known about brain responses which index processing in other linguistic domains such as lexicon, semantics and syntax in this group. The present study…

  10. “Pre-semantic” cognition revisited: Critical differences between semantic aphasia and semantic dementia

    PubMed Central

    Jefferies, Elizabeth; Rogers, Timothy T.; Hopper, Samantha; Lambon Ralph, Matthew A.

    2009-01-01

    Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal “pre-semantic” tasks: e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are atypical of the domain and “regularisation errors” (irregular/atypical items are produced as if they were domain-typical). The emergence of this pattern across diverse tasks in the same patients indicates that semantic memory plays a key role in all of these types of “pre-semantic” processing. However, this claim remains controversial because semantically-impaired patients sometimes fail to show an influence of regularity. This study demonstrates that (a) the location of brain damage and (b) the underlying nature of the semantic deficit affect the likelihood of observing the expected relationship between poor comprehension and regularity effects. We compared the effect of multimodal semantic impairment in the context of semantic dementia and stroke aphasia on the seven “pre-semantic” tasks listed above. In all of these tasks, the semantic aphasia patients were less sensitive to typicality than the semantic dementia patients, even though the two groups obtained comparable scores on semantic tests. The semantic aphasia group also made fewer regularisation errors and many more unrelated and perseverative responses. We propose that these group differences reflect the different locus for the semantic impairment in the two conditions: patients with semantic dementia have degraded semantic representations, whereas semantic aphasia patients show deregulated semantic cognition with concomitant executive deficits. These findings suggest a reinterpretation of single case studies of comprehension-impaired aphasic patients who fail to show the expected effect of regularity on “pre-semantic” tasks. Consequently, such

  11. Representations for Semantic Learning Webs: Semantic Web Technology in Learning Support

    ERIC Educational Resources Information Center

    Dzbor, M.; Stutt, A.; Motta, E.; Collins, T.

    2007-01-01

    Recent work on applying semantic technologies to learning has concentrated on providing novel means of accessing and making use of learning objects. However, this is unnecessarily limiting: semantic technologies will make it possible to develop a range of educational Semantic Web services, such as interpretation, structure-visualization, support…

  12. SSWAP: A Simple Semantic Web Architecture and Protocol for Semantic Web Services

    USDA-ARS?s Scientific Manuscript database

    SSWAP (Simple Semantic Web Architecture and Protocol) is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP is the driving technology behind the Virtual Plant Information Network, an NSF-funded semantic w...

  13. Representations for Semantic Learning Webs: Semantic Web Technology in Learning Support

    ERIC Educational Resources Information Center

    Dzbor, M.; Stutt, A.; Motta, E.; Collins, T.

    2007-01-01

    Recent work on applying semantic technologies to learning has concentrated on providing novel means of accessing and making use of learning objects. However, this is unnecessarily limiting: semantic technologies will make it possible to develop a range of educational Semantic Web services, such as interpretation, structure-visualization, support…

  14. Explaining semantic short-term memory deficits: Evidence for the critical role of semantic control

    PubMed Central

    Hoffman, Paul; Jefferies, Elizabeth; Lambon Ralph, Matthew A.

    2011-01-01

    Patients with apparently selective short-term memory (STM) deficits for semantic information have played an important role in developing multi-store theories of STM and challenge the idea that verbal STM is supported by maintaining activation in the language system. We propose that semantic STM deficits are not as selective as previously thought and can occur as a result of mild disruption to semantic control processes, i.e., mechanisms that bias semantic processing towards task-relevant aspects of knowledge and away from irrelevant information. We tested three semantic STM patients with tasks that tapped four aspects of semantic control: (i) resolving ambiguity between word meanings, (ii) sensitivity to cues, (iii) ignoring irrelevant information and (iv) detecting weak semantic associations. All were impaired in conditions requiring more semantic control, irrespective of the STM demands of the task, suggesting a mild, but task-general, deficit in regulating semantic knowledge. This mild deficit has a disproportionate effect on STM tasks because they have high intrinsic control demands: in STM tasks, control is required to keep information active when it is no longer available in the environment and to manage competition between items held in memory simultaneously. By re-interpreting the core deficit in semantic STM patients in this way, we are able to explain their apparently selective impairment without the need for a specialised STM store. Instead, we argue that semantic STM patients occupy the mildest end of spectrum of semantic control disorders. PMID:21195105

  15. Synonyms Provide Semantic Preview Benefit in English

    PubMed Central

    Schotter, Elizabeth R.

    2013-01-01

    While orthographic and phonological preview benefits in reading are uncontroversial (see Schotter, Angele, & Rayner, 2012 for a review), researchers have debated the existence of semantic preview benefit with positive evidence in Chinese and German, but no support in English. Two experiments, using the gazecontingent boundary paradigm (Rayner, 1975), show that semantic preview benefit can be observed in English when the preview and target are synonyms (share the same or highly similar meaning, e.g., curlers-rollers). However, no semantic preview benefit was observed for semantic associates (e.g., curlers-styling). These different preview conditions represent different degrees to which the meaning of the sentence changes when the preview is replaced by the target. When this continuous variable (determined by a norming procedure) was used as the predictor in the analyses, there was a significant relationship between it and all reading time measures, suggesting that similarity in meaning between what is accessed parafoveally and what is processed foveally may be an important influence on the presence of semantic preview benefit. Why synonyms provide semantic preview benefit in reading English is discussed in relation to (1) previous failures to find semantic preview benefit in English and (2) the fact that semantic preview benefit is observed in other languages even for non-synonymous words. Semantic preview benefit is argued to depend on several factors—attentional resources, depth of orthography, and degree of similarity between preview and target. PMID:24347813

  16. Neural correlates underlying musical semantic memory.

    PubMed

    Groussard, M; Viader, F; Landeau, B; Desgranges, B; Eustache, F; Platel, H

    2009-07-01

    Numerous functional imaging studies have examined the neural basis of semantic memory mainly using verbal and visuospatial materials. Musical material also allows an original way to explore semantic memory processes. We used PET imaging to determine the neural substrates that underlie musical semantic memory using different tasks and stimuli. The results of three PET studies revealed a greater involvement of the anterior part of the temporal lobe. Concerning clinical observations and our neuroimaging data, the musical lexicon (and most widely musical semantic memory) appears to be sustained by a temporo-prefrontal cerebral network involving right and left cerebral regions.

  17. Semantic Clustering of Search Engine Results

    PubMed Central

    Soliman, Sara Saad; El-Sayed, Maged F.; Hassan, Yasser F.

    2015-01-01

    This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision. PMID:26933673

  18. Similarity Based Semantic Web Service Match

    NASA Astrophysics Data System (ADS)

    Peng, Hui; Niu, Wenjia; Huang, Ronghuai

    Semantic web service discovery aims at returning the most matching advertised services to the service requester by comparing the semantic of the request service with an advertised service. The semantic of a web service are described in terms of inputs, outputs, preconditions and results in Ontology Web Language for Service (OWL-S) which formalized by W3C. In this paper we proposed an algorithm to calculate the semantic similarity of two services by weighted averaging their inputs and outputs similarities. Case study and applications show the effectiveness of our algorithm in service match.

  19. Human Cytomegalovirus Manipulation of Latently Infected Cells

    PubMed Central

    Sinclair, John H.; Reeves, Matthew B.

    2013-01-01

    Primary infection with human cytomegalovirus (HCMV) results in the establishment of a lifelong infection of the host which is aided by the ability of HCMV to undergo a latent infection. One site of HCMV latency in vivo is in haematopoietic progenitor cells, resident in the bone marrow, with genome carriage and reactivation being restricted to the cells of the myeloid lineage. Until recently, HCMV latency has been considered to be relatively quiescent with the virus being maintained essentially as a “silent partner” until conditions are met that trigger reactivation. However, advances in techniques to study global changes in gene expression have begun to show that HCMV latency is a highly active process which involves expression of specific latency-associated viral gene products which orchestrate major changes in the latently infected cell. These changes are argued to help maintain latent infection and to modulate the cellular environment to the benefit of latent virus. In this review, we will discuss these new findings and how they impact not only on our understanding of the biology of HCMV latency but also how they could provide tantalising glimpses into mechanisms that could become targets for the clearance of latent HCMV. PMID:24284875

  20. Cellular Localization of Latent Murine Cytomegalovirus

    PubMed Central

    Koffron, Alan J.; Hummel, Mary; Patterson, Bruce K.; Yan, Shixian; Kaufman, Dixon B.; Fryer, Jonathan P.; Stuart, Frank P.; Abecassis, Michael I.

    1998-01-01

    Herpesviruses typically establish latent infection in their hosts. The cell(s) responsible for harboring latent virus, in most cases, is not known. Using immunofluorescence and PCR-in situ hybridization (PISH), a technique which combines the sensitivity of PCR with the localization and specificity of in situ hybridization, we provide the first direct evidence that endothelial cells are a major site of murine cytomegalovirus (MCMV) DNA in latently infected animals. These findings are consistent with existing knowledge of the biological behavior of CMV, in particular the transmission of latent CMV by solid organ and bone marrow transplantation, in both human and animal models. In addition, we have localized MCMV DNA in the lung alveolar macrophage and in bone marrow cells. Our findings confirm that bone marrow-derived hematopoietic cells are a site of CMV latency and further suggest that bone marrow may be a reservoir of infected progeny capable of migrating into the circulation and establishing latency in various tissues. These findings provide clearly needed insight into the site of latent infection which is central to an understanding of the mechanisms of reactivation. PMID:9420204