Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or

Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations.

PubMed

Munkhdalai, Tsendsuren; Li, Meijing; Batsuren, Khuyagbaatar; Park, Hyeon Ah; Choi, Nak Hyeon; Ryu, Keun Ho

2015-01-01

Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.

Transfer learning for biomedical named entity recognition with neural networks.

PubMed

Giorgi, John M; Bader, Gary D

2018-06-01

The explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases, and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER. We demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target data sets with a small number of labels (approximately 6000 or less). Source code for the LSTM-CRF is available athttps://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available athttps://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/. john.giorgi@utoronto.ca. Supplementary data are available at Bioinformatics online.

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text

PubMed Central

Yu, Jian

2017-01-01

Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems. PMID:29065612

TaggerOne: joint named entity recognition and normalization with semi-Markov Models

PubMed Central

Leaman, Robert; Lu, Zhiyong

2016-01-01

Motivation: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. Methods: We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. Results: We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. Availability and Implementation: The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone Contact: zhiyong.lu@nih.gov Supplementary information: Supplementary data are

TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

PubMed

Leaman, Robert; Lu, Zhiyong

2016-09-15

Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone zhiyong.lu@nih.gov Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written

A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

PubMed Central

2017-01-01

Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations. PMID:28644863

Anatomical Entity Recognition with a Hierarchical Framework Augmented by External Resources

PubMed Central

Xu, Yan; Hua, Ji; Ni, Zhaoheng; Chen, Qinlang; Fan, Yubo; Ananiadou, Sophia; Chang, Eric I-Chao; Tsujii, Junichi

2014-01-01

References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available. PMID:25343498

Segregation of anterior temporal regions critical for retrieving names of unique and nonunique entities reflects underlying long-range connectivity

PubMed Central

Mehta, Sonya; Inoue, Kayo; Rudrauf, David; Damasio, Hanna; Tranel, Daniel; Grabowski, Thomas

2015-01-01

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing and

Named Entity Recognition in a Hungarian NL Based QA System

NASA Astrophysics Data System (ADS)

Tikkl, Domonkos; Szidarovszky, P. Ferenc; Kardkovacs, Zsolt T.; Magyar, Gábor

In WoW project our purpose is to create a complex search interface with the following features: search in the deep web content of contracted partners' databases, processing Hungarian natural language (NL) questions and transforming them to SQL queries for database access, image search supported by a visual thesaurus that describes in a structural form the visual content of images (also in Hungarian). This paper primarily focuses on a particular problem of question processing task: the entity recognition. Before going into details we give a short overview of the project's aims.

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

PubMed

Urbain, Jay

2015-12-01

We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results. Copyright © 2015 Elsevier Inc. All rights reserved.

Segregation of anterior temporal regions critical for retrieving names of unique and non-unique entities reflects underlying long-range connectivity.

PubMed

Mehta, Sonya; Inoue, Kayo; Rudrauf, David; Damasio, Hanna; Tranel, Daniel; Grabowski, Thomas

2016-02-01

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category-specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus (UNC) was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus (ILF) was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing

LeadMine: a grammar and dictionary driven approach to entity recognition.

PubMed

Lowe, Daniel M; Sayle, Roger A

2015-01-01

Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution.

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

Impact of translation on named-entity recognition in radiology texts

PubMed Central

Pedro, Vasco

2017-01-01

Abstract Radiology reports describe the results of radiography procedures and have the potential of being a useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. We created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semi-automatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD we studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms extracted from human translations as our gold standard, we calculated how similar to this standard were the terms extracted using other translations. We found that a completely automatic translation approach using Google leads to F-scores (between 0.861 and 0.868, depending on the extraction approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results we also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. Database URL: https://github.com/lasigeBioTM/MRRAD PMID:29220455

Entity recognition in the biomedical domain using a hybrid approach.

PubMed

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

LeadMine: a grammar and dictionary driven approach to entity recognition

PubMed Central

2015-01-01

Background Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Conclusions Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution. PMID:25810776

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition.

PubMed

Tsai, Richard Tzong-Han; Sung, Cheng-Lung; Dai, Hong-Jie; Hung, Hsieh-Chuan; Sung, Ting-Yi; Hsu, Wen-Lian

2006-12-18

Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing. To develop our ML-based Bio-NER system, we employ conditional

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

PubMed

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

Applying Suffix Rules to Organization Name Recognition

NASA Astrophysics Data System (ADS)

Inui, Takashi; Murakami, Koji; Hashimoto, Taiichi; Utsumi, Kazuo; Ishikawa, Masamichi

This paper presents a method for boosting the performance of the organization name recognition, which is a part of named entity recognition (NER). Although gazetteers (lists of the NEs) have been known as one of the effective features for supervised machine learning approaches on the NER task, the previous methods which have applied the gazetteers to the NER were very simple. The gazetteers have been used just for searching the exact matches between input text and NEs included in them. The proposed method generates regular expression rules from gazetteers, and, with these rules, it can realize a high-coverage searches based on looser matches between input text and NEs. To generate these rules, we focus on the two well-known characteristics of NE expressions; 1) most of NE expressions can be divided into two parts, class-reference part and instance-reference part, 2) for most of NE expressions the class-reference parts are located at the suffix position of them. A pattern mining algorithm runs on the set of NEs in the gazetteers, and some frequent word sequences from which NEs are constructed are found. Then, we employ only word sequences which have the class-reference part at the suffix position as suffix rules. Experimental results showed that our proposed method improved the performance of the organization name recognition, and achieved the 84.58 F-value for evaluation data.

Developing a hybrid dictionary-based bio-entity recognition technique.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Developing a hybrid dictionary-based bio-entity recognition technique

PubMed Central

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

PubMed

Akhondi, Saber A; Hettne, Kristina M; van der Horst, Eelke; van Mulligen, Erik M; Kors, Jan A

2015-01-01

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming

Recognition of chemical entities: combining dictionary-based and grammar-based approaches

PubMed Central

2015-01-01

Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named

A method for named entity normalization in biomedical articles: application to diseases and plants.

PubMed

Cho, Hyejin; Choi, Wonjun; Lee, Hyunju

2017-10-13

In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles. After NER is applied to articles, the next step is to normalize the identified names into standard concepts (i.e., disease names are mapped to the National Library of Medicine's Medical Subject Headings disease terms). In biomedical articles, many entity normalization methods rely on domain-specific dictionaries for resolving synonyms and abbreviations. However, the dictionaries are not comprehensive except for some entities such as genes. In recent years, biomedical articles have accumulated rapidly, and neural network-based algorithms that incorporate a large amount of unlabeled data have shown considerable success in several natural language processing problems. In this study, we propose an approach for normalizing biological entities, such as disease names and plant names, by using word embeddings to represent semantic spaces. For diseases, training data from the National Center for Biotechnology Information (NCBI) disease corpus and unlabeled data from PubMed abstracts were used to construct word representations. For plants, a training corpus that we manually constructed and unlabeled PubMed abstracts were used to represent word vectors. We showed that the proposed approach performed better than the use of only the training corpus or only the unlabeled data and showed that the normalization accuracy was improved by using our model even when the dictionaries were not comprehensive. We obtained F-scores of 0.808 and 0.690 for normalizing the NCBI disease corpus and manually constructed plant corpus, respectively. We further evaluated our approach using a data set in the disease normalization task of the BioCreative V challenge. When only the disease corpus was used as a dictionary, our approach significantly outperformed the best system of the task. The proposed approach shows robust

Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization.

PubMed

Dai, Hong-Jie; Lai, Po-Ting; Chang, Yung-Chun; Tsai, Richard Tzong-Han

2015-01-01

The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. To extract knowledge from the extensive literatures on such compounds and drugs, the organizers of BioCreative IV administered the CHEMical Compound and Drug Named Entity Recognition (CHEMDNER) task to establish a standard dataset for evaluating state-of-the-art chemical entity recognition methods. This study introduces the approach of our CHEMDNER system. Instead of emphasizing the development of novel feature sets for machine learning, this study investigates the effect of various tag schemes on the recognition of the names of chemicals and drugs by using conditional random fields. Experiments were conducted using combinations of different tokenization strategies and tag schemes to investigate the effects of tag set selection and tokenization method on the CHEMDNER task. This study presents the performance of CHEMDNER of three more representative tag schemes-IOBE, IOBES, and IOB12E-when applied to a widely utilized IOB tag set and combined with the coarse-/fine-grained tokenization methods. The experimental results thus reveal that the fine-grained tokenization strategy performance best in terms of precision, recall and F-scores when the IOBES tag set was utilized. The IOBES model with fine-grained tokenization yielded the best-F-scores in the six chemical entity categories other than the "Multiple" entity category. Nonetheless, no significant improvement was observed when a more representative tag schemes was used with the coarse or fine-grained tokenization rules. The best F-scores that were achieved using the developed system on the test dataset of the CHEMDNER task were 0.833 and 0.815 for the chemical documents indexing and the chemical entity mention recognition tasks, respectively. The results herein highlight the importance

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

PubMed

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-05-01

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. andyli@ece.ufl.edu or aconesa@ufl.edu. Supplementary data are available at Bioinformatics online.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

PubMed Central

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-01-01

Abstract Motivation Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Contact andyli@ece.ufl.edu or aconesa@ufl.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29272325

Network analysis of named entity co-occurrences in written texts

NASA Astrophysics Data System (ADS)

Amancio, Diego Raphael

2016-06-01

The use of methods borrowed from statistics and physics to analyze written texts has allowed the discovery of unprecedent patterns of human behavior and cognition by establishing links between models features and language structure. While current models have been useful to unveil patterns via analysis of syntactical and semantical networks, only a few works have probed the relevance of investigating the structure arising from the relationship between relevant entities such as characters, locations and organizations. In this study, we represent entities appearing in the same context as a co-occurrence network, where links are established according to a null model based on random, shuffled texts. Computational simulations performed in novels revealed that the proposed model displays interesting topological features, such as the small world feature, characterized by high values of clustering coefficient. The effectiveness of our model was verified in a practical pattern recognition task in real networks. When compared with traditional word adjacency networks, our model displayed optimized results in identifying unknown references in texts. Because the proposed representation plays a complementary role in characterizing unstructured documents via topological analysis of named entities, we believe that it could be useful to improve the characterization of written texts (and related systems), specially if combined with traditional approaches based on statistical and deeper paradigms.

NELasso: Group-Sparse Modeling for Characterizing Relations Among Named Entities in News Articles.

PubMed

Tariq, Amara; Karim, Asim; Foroosh, Hassan

2017-10-01

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities' network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time.

A System for Identifying Named Entities in Biomedical Text: how Results From two Evaluations Reflect on Both the System and the Evaluations

PubMed Central

Dingare, Shipra; Nissim, Malvina; Finkel, Jenny; Grover, Claire

2005-01-01

We present a maximum entropy-based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F-score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal. PMID:18629295

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 3 2013-01-01 2013-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 3 2012-01-01 2012-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 3 2011-01-01 2011-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 3 2014-01-01 2014-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 3 2010-01-01 2010-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

Biomedical named entity extraction: some issues of corpus compatibilities.

PubMed

Ekbal, Asif; Saha, Sriparna; Sikdar, Utpal Kumar

2013-01-01

Named Entity (NE) extraction is one of the most fundamental and important tasks in biomedical information extraction. It involves identification of certain entities from text and their classification into some predefined categories. In the biomedical community, there is yet no general consensus regarding named entity (NE) annotation; thus, it is very difficult to compare the existing systems due to corpus incompatibilities. Due to this problem we can not also exploit the advantages of using different corpora together. In our present work we address the issues of corpus compatibilities, and use a single objective optimization (SOO) based classifier ensemble technique that uses the search capability of genetic algorithm (GA) for NE extraction in biomedicine. We hypothesize that the reliability of predictions of each classifier differs among the various output classes. We use Conditional Random Field (CRF) and Support Vector Machine (SVM) frameworks to build a number of models depending upon the various representations of the set of features and/or feature templates. It is to be noted that we tried to extract the features without using any deep domain knowledge and/or resources. In order to assess the challenges of corpus compatibilities, we experiment with the different benchmark datasets and their various combinations. Comparison results with the existing approaches prove the efficacy of the used technique. GA based ensemble achieves around 2% performance improvements over the individual classifiers. Degradation in performance on the integrated corpus clearly shows the difficulties of the task. In summary, our used ensemble based approach attains the state-of-the-art performance levels for entity extraction in three different kinds of biomedical datasets. The possible reasons behind the better performance in our used approach are the (i). use of variety and rich features as described in Subsection "Features for named entity extraction"; (ii) use of GA based

A study of active learning methods for named entity recognition in clinical text.

PubMed

Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

2015-12-01

Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

PubMed Central

Gan, Liang; Cheng, Mian; Wu, Quanyuan

2018-01-01

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking. PMID:29849994

Entity-based Stochastic Analysis of Search Results for Query Expansion and Results Re-Ranking

DTIC Science & Technology

2015-11-20

pages) and struc- tured data (e.g. Linked Open Data ( LOD ) [8]) coexist in var- ious forms. An important observation is that entity names (like names of...the top-L (e.g. L = 1, 000) results are retrieved. Then, Named Entity Recognition (NER) is applied in these results for identifying LOD entities. In...the next (optional) step, more semantic information about the identified entities is retrieved from the LOD (like properties and related entities). A

Anatomical entity mention recognition at literature scale

PubMed Central

Pyysalo, Sampo; Ananiadou, Sophia

2014-01-01

Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24162468

77 FR 70163 - Recognition of Entities for the Accreditation of Qualified Health Plans

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-23

... DEPARTMENT OF HEALTH AND HUMAN SERVICES [CMS-9961-N] Recognition of Entities for the Accreditation... as recognized accrediting entities for the purposes of fulfilling the accreditation requirement as... a recognized accrediting entity on a uniform timeline established by the applicable Exchange. On...

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

PubMed

Bachman, John A; Gyori, Benjamin M; Sorger, Peter K

2018-06-28

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

PubMed

Jauregi Unanue, Iñigo; Zare Borzeshi, Ehsan; Piccardi, Massimo

2017-12-01

Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary. Copyright © 2017 Elsevier Inc. All rights reserved.

Discovery of Predicate-Oriented Relations among Named Entities Extracted from Thai Texts

NASA Astrophysics Data System (ADS)

Tongtep, Nattapong; Theeramunkong, Thanaruk

Extracting named entities (NEs) and their relations is more difficult in Thai than in other languages due to several Thai specific characteristics, including no explicit boundaries for words, phrases and sentences; few case markers and modifier clues; high ambiguity in compound words and serial verbs; and flexible word orders. Unlike most previous works which focused on NE relations of specific actions, such as work_for, live_in, located_in, and kill, this paper proposes more general types of NE relations, called predicate-oriented relation (PoR), where an extracted action part (verb) is used as a core component to associate related named entities extracted from Thai Texts. Lacking a practical parser for the Thai language, we present three types of surface features, i.e. punctuation marks (such as token spaces), entity types and the number of entities and then apply five alternative commonly used learning schemes to investigate their performance on predicate-oriented relation extraction. The experimental results show that our approach achieves the F-measure of 97.76%, 99.19%, 95.00% and 93.50% on four different types of predicate-oriented relation (action-location, location-action, action-person and person-action) in crime-related news documents using a data set of 1,736 entity pairs. The effects of NE extraction techniques, feature sets and class unbalance on the performance of relation extraction are explored.

Recognition of names of eminent psychologists.

PubMed

Duncan, C P

1976-10-01

Faculty members, graduate students, undergraduate majors, and introductory psychology students checked those names they recognized in the list of 228 deceased psychologists, rated for eminence, provided by Annin, Boring, and Watson. Mean percentage recognition was less than 50% for the 128 American psychologists, and less than 25% for the 100 foreign psychologists, by the faculty subjects. The other three groups of subjects gave even lower recognition scores. Recognition was probably also influenced by recency; median year of death of the American psychologists was 1955, of the foreign psychologists, 1943. High recognition (defined as recognition by 80% or more of the faculty group) was achieved by only 34 psychologists, almost all of them American. These highly recognized psychologists also had high eminence ratings, but there was an equal number of psychologists with high eminence ratings that were poorly recognized.

Identification of related gene/protein names based on an HMM of name variations.

PubMed

Yeganova, L; Smith, L; Wilbur, W J

2004-04-01

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

Rapid Naming Speed and Chinese Character Recognition

ERIC Educational Resources Information Center

Liao, Chen-Huei; Georgiou, George K.; Parrila, Rauno

2008-01-01

We examined the relationship between rapid naming speed (RAN) and Chinese character recognition accuracy and fluency. Sixty-three grade 2 and 54 grade 4 Taiwanese children were administered four RAN tasks (colors, digits, Zhu-Yin-Fu-Hao, characters), and two character recognition tasks. RAN tasks accounted for more reading variance in grade 4 than…

Effects of Minority Status on Facial Recognition and Naming Performance.

ERIC Educational Resources Information Center

Roberts, Richard J.; Hamsher, Kerry

1984-01-01

Examined the differential effects of minority status in Blacks (N=94) on a facial recognition test and a naming test. Results showed that performance on the facial recognition test was relatively free of racial bias, but this was not the case for visual naming. (LLL)

A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

PubMed Central

Basaruddin, T.

2016-01-01

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645. PMID:27843447

Recognition of Famous Names in Psychology by Students and Staff.

ERIC Educational Resources Information Center

Bunnell, Julie K.

1992-01-01

Presents results of a name recognition questionnaire testing the historical awareness of psychology majors and faculty members. Reports that students showed a low level of name recognition prior to taking a course in the history of psychology. Concludes that explicit instruction is required to impart knowledge of the history of the discipline. (DK)

Naming unique entities in the semantic variant of primary progressive aphasia and Alzheimer's disease: Towards a better understanding of the semantic impairment.

PubMed

Montembeault, M; Brambati, S M; Joubert, S; Boukadi, M; Chapleau, M; Laforce, R Jr; Wilson, M A; Macoir, J; Rouleau, I

2017-01-27

While the semantic variant of primary progressive aphasia (svPPA) is characterized by a predominant semantic memory impairment, episodic memory impairments are the clinical hallmark of Alzheimer's disease (AD). However, AD patients also present with semantic deficits, which are more severe for semantically unique entities (e.g. a famous person) than for common concepts (e.g. a beaver). Previous studies in these patient populations have largely focused on famous-person naming. Therefore, we aimed to evaluate if these impairments also extend to other semantically unique entities such as famous places and famous logos. In this study, 13 AD patients, 9 svPPA patients, and 12 cognitively unimpaired elderly subjects (CTRL) were tested with a picture-naming test of non-unique entities (Boston Naming Test) and three experimental tests of semantically unique entities assessing naming of famous persons, places, and logos. Both clinical groups were overall more impaired at naming semantically unique entities than non-unique entities. Naming impairments in AD and svPPA extended to the other types of semantically unique entities, since a CTRL>AD>svPPA pattern was found on the performance of all naming tests. Naming famous places and famous persons appeared to be most impaired in svPPA, and both specific and general semantic knowledge for these entities were affected in these patients. Although AD patients were most significantly impaired on famous-person naming, only their specific semantic knowledge was impaired, while general knowledge was preserved. Post-hoc neuroimaging analyses also showed that famous-person naming impairments in AD correlated with atrophy in the temporo-parietal junction, a region functionally associated with lexical access. In line with previous studies, svPPA patients' impairment in both naming and semantic knowledge suggest a more profound semantic impairment, while naming impairments in AD may arise to a greater extent from impaired lexical access

Does humor in radio advertising affect recognition of novel product brand names?

PubMed

Berg, E M; Lippman, L G

2001-04-01

The authors proposed that item selection during shopping is based on brand name recognition rather than recall. College students rated advertisements and news stories of a simulated radio program for level of amusement (orienting activity) before participating in a surprise recognition test. Humor level of the advertisements was varied systematically, and content was controlled. According to signal detection analysis, humor did not affect the strength of recognition memory for brand names (nonsense units). However, brand names and product types were significantly more likely to be associated when appearing in humorous advertisements than in nonhumorous advertisements. The results are compared with prior findings concerning humor and recall.

Identifying non-elliptical entity mentions in a coordinated NP with ellipses.

PubMed

Chae, Jeongmin; Jung, Younghee; Lee, Taemin; Jung, Soonyoung; Huh, Chan; Kim, Gilhan; Kim, Hyeoncheol; Oh, Heungbum

2014-02-01

Named entities in the biomedical domain are often written using a Noun Phrase (NP) along with a coordinating conjunction such as 'and' and 'or'. In addition, repeated words among named entity mentions are frequently omitted. It is often difficult to identify named entities. Although various Named Entity Recognition (NER) methods have tried to solve this problem, these methods can only deal with relatively simple elliptical patterns in coordinated NPs. We propose a new NER method for identifying non-elliptical entity mentions with simple or complex ellipses using linguistic rules and an entity mention dictionary. The GENIA and CRAFT corpora were used to evaluate the performance of the proposed system. The GENIA corpus was used to evaluate the performance of the system according to the quality of the dictionary. The GENIA corpus comprises 3434 non-elliptical entity mentions in 1585 coordinated NPs with ellipses. The system achieves 92.11% precision, 95.20% recall, and 93.63% F-score in identification of non-elliptical entity mentions in coordinated NPs. The accuracy of the system in resolving simple and complex ellipses is 94.54% and 91.95%, respectively. The CRAFT corpus was used to evaluate the performance of the system under realistic conditions. The system achieved 78.47% precision, 67.10% recall, and 72.34% F-score in coordinated NPs. The performance evaluations of the system show that it efficiently solves the problem caused by ellipses, and improves NER performance. The algorithm is implemented in PHP and the code can be downloaded from https://code.google.com/p/medtextmining/. Copyright © 2013. Published by Elsevier Inc.

Naming and recognizing famous faces in temporal lobe epilepsy.

PubMed

Glosser, G; Salvucci, A E; Chiaravalloti, N D

2003-07-08

To assess naming and recognition of faces of familiar famous people in patients with epilepsy before and after anterior temporal lobectomy (ATL). Color photographs of famous people were presented for naming and description to 63 patients with temporal lobe epilepsy (TLE) either before or after ATL and to 10 healthy age- and education-matched controls. Spontaneous naming of photographed famous people was impaired in all patient groups, but was most abnormal in patients who had undergone left ATL. When allowed to demonstrate knowledge of the famous faces through verbal descriptions, rather than naming, patients with left TLE, left ATL, and right TLE improved to normal levels, but patients with right ATL were still impaired, suggesting a new deficit in identifying famous faces. Naming of famous people was related to naming of other common objects, verbal memory, and perceptual discrimination of faces. Recognition of the identity of pictured famous people was more related to visuospatial perception and memory. Lesions in anterior regions of the right temporal lobe impair recognition of the identities of familiar faces, as well as the learning of new faces. Lesions in the left temporal lobe, especially in anterior regions, disrupt access to the names of known people, but do not affect recognition of the identities of famous faces. Results are consistent with the hypothesized role of lateralized anterior temporal lobe structures in facial recognition and naming of unique entities.

Recognition of cigarette brand names and logos by primary schoolchildren in Ankara, Turkey

PubMed Central

Emri, S.; Bagci, T.; Karakoca, Y.; Baris, E.

1998-01-01

OBJECTIVE—To assess the smoking behaviour of primary schoolchildren and their ability to recognise brand names and logos of widely advertised cigarettes, compared with other commercial products intended for children.
DESIGN—Cross-sectional survey in classroom settings using a questionnaire designed to measure attitudes towards smoking and the recognition of brand names and logos for 16 food, beverage, cigarette, and toothpaste products.
SETTING—Ankara, Turkey.
SUBJECTS—1093 children (54.6% boys, 44.4% girls) aged 7-13 years (mean = 10, SD = 1), from grades 2-5. The student sample was taken from three primary schools—one school in each of three residential districts representing high, middle, and low income populations.
MAIN OUTCOME MEASURES—Prevalence of ever-smoking, recognition of brand names and logos.
RESULTS—Prevalence of ever-smoking was 11.7% overall (13.9% among boys and 9.1% among girls; p<0.05). Children aged eight years or less had a higher prevalence of ever-smoking (19.6%) than older children (p<0.002). Ever-smoking prevalence did not differ significantly across the three school districts. Ever-smoking prevalence was higher among children with at least one parent who smoked (15.3%) than among those whose parents did not (4.8%) (p<0.001). Brand recognition rates ranged from 58.1% for Chee-tos (a food product) to 95.2% for Samsun (a Turkish cigarette brand). Recognition rates for cigarette brand names and logos were 95.2% and 80.8%, respectively, for Samsun; 84.0% and 90.5%, respectively, for Camel; and 92.1% and 69.5%, respectively, for Marlboro. The Camel logo and the Samsun and Marlboro brand names were the most highly recognised of all product logos and brand names tested.
CONCLUSIONS—The high recognition of cigarette brand names and logos is most likely the result of tobacco advertising and promotion. Our results indicate the need to implement comprehensive tobacco control measures in Turkey

Recognition of cigarette brand names and logos by primary schoolchildren in Ankara, Turkey.

PubMed

Emri, S; Bağci, T; Karakoca, Y; Bariş, E

1998-01-01

To assess the smoking behaviour of primary schoolchildren and their ability to recognise brand names and logos of widely advertised cigarettes, compared with other commercial products intended for children. Cross-sectional survey in classroom settings using a questionnaire designed to measure attitudes towards smoking and the recognition of brand names and logos for 16 food, beverage, cigarette, and toothpaste products. Ankara, Turkey. 1093 children (54.6% boys, 44.4% girls) aged 7-13 years (mean = 10, SD = 1), from grades 2-5. The student sample was taken from three primary schools--one school in each of three residential districts representing high, middle, and low income populations. Prevalence of ever-smoking, recognition of brand names and logos. Prevalence of ever-smoking was 11.7% overall (13.9% among boys and 9.1% among girls; p < 0.05). Children aged eight years or less had a higher prevalence of ever-smoking (19.6%) than older children (p < 0.002). Ever-smoking prevalence did not differ significantly across the three school districts. Ever-smoking prevalence was higher among children with at least one parent who smoked (15.3%) than among those whose parents did not (4.8%) (p < 0.001). Brand recognition rates ranged from 58.1% for Chee-tos (a food product) to 95.2% for Samsun (a Turkish cigarette brand). Recognition rates for cigarette brand names and logos were 95.2% and 80.8%, respectively, for Samsun; 84.0% and 90.5%, respectively, for Camel; and 92.1% and 69.5%, respectively, for Marlboro. The Camel logo and the Samsun and Marlboro brand names were the most highly recognised of all product logos and brand names tested. The high recognition of cigarette brand names and logos is most likely the result of tobacco advertising and promotion. Our results indicate the need to implement comprehensive tobacco control measures in Turkey.

False recall and recognition of brand names increases over time.

PubMed

Sherman, Susan M

2013-01-01

Using the Deese-Roediger-McDermott (DRM) paradigm, participants are presented with lists of associated words (e.g., bed, awake, night). Subsequently, they reliably have false memories for related but nonpresented words (e.g., SLEEP). Previous research has found that false memories can be created for brand names (e.g., Morrisons, Sainsbury's, Waitrose, and TESCO). The present study investigates the effect of a week's delay on false memories for brand names. Participants were presented with lists of brand names followed by a distractor task. In two between-subjects experiments, participants completed a free recall task or a recognition task either immediately or a week later. In two within-subjects experiments, participants completed a free recall task or a recognition task both immediately and a week later. Correct recall for presented list items decreased over time, whereas false recall for nonpresented lure items increased. For recognition, raw scores revealed an increase in false memory across time reflected in an increase in Remember responses. Analysis of Pr scores revealed that false memory for lures stayed constant over a week, but with an increase in Remember responses in the between-subjects experiment and a trend in the same direction in the within-subjects experiment. Implications for theories of false memory are discussed.

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

PubMed Central

Chun, Hong-Woo; Tsuruoka, Yoshimasa; Kim, Jin-Dong; Shiba, Rie; Nagata, Naoki; Hishiki, Teruyoshi; Tsujii, Jun'ichi

2006-01-01

Background Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. Methods We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Results Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. Conclusion A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques. PMID:17134477

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

PubMed

Chun, Hong-Woo; Tsuruoka, Yoshimasa; Kim, Jin-Dong; Shiba, Rie; Nagata, Naoki; Hishiki, Teruyoshi; Tsujii, Jun'ichi

2006-11-24

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

Abstracts versus Full Texts and Patents: A Quantitative Analysis of Biomedical Entities

NASA Astrophysics Data System (ADS)

Müller, Bernd; Klinger, Roman; Gurulingappa, Harsha; Mevissen, Heinz-Theodor; Hofmann-Apitius, Martin; Fluck, Juliane; Friedrich, Christoph M.

In information retrieval, named entity recognition gives the opportunity to apply semantic search in domain specific corpora. Recently, more full text patents and journal articles became freely available. As the information distribution amongst the different sections is unknown, an analysis of the diversity is of interest.

Disambiguating the species of biomedical named entities using natural language parsers

PubMed Central

Wang, Xinglong; Tsujii, Jun'ichi; Ananiadou, Sophia

2010-01-01

Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers. Results: We build a corpus for organism disambiguation where every occurrence of protein/gene entity is manually tagged with a species ID, and evaluate a number of methods on it. Promising results are obtained by training a machine learning model on syntactic parse trees, which is then used to decide whether an entity belongs to the model organism denoted by a neighbouring species-indicating word (e.g. yeast). The parser-based approaches are also compared with a supervised classification method and results indicate that the former are a more favorable choice when domain portability is of concern. The best overall performance is obtained by combining the strengths of syntactic features and supervised classification. Availability: The corpus and demo are available at http://www.nactem.ac.uk/deca_details/start.cgi, and the software is freely available as U-Compare components (Kano et al., 2009): NaCTeM Species Word Detector and NaCTeM Species Disambiguator. U-Compare is available at http://-compare.org/ Contact: xinglong.wang@manchester.ac.uk PMID:20053840

Chemical entity recognition in patents by combining dictionary-based and statistical approaches

PubMed Central

Akhondi, Saber A.; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F.H.; Hettne, Kristina M.; van Mulligen, Erik M.; Kors, Jan A.

2016-01-01

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small. Database URL: http://biosemantics.org/chemdner-patents PMID:27141091

Famous face recognition and naming test: a normative study.

PubMed

Rizzo, S; Venneri, A; Papagno, C

2002-10-01

Tests of famous face recognition and naming, and tasks assessing semantic knowledge about famous people after presentation either of their faces or their names are often used in the neuropsychological examination of aphasic, amnesic and demented patients. A total of 187 normal subjects took part in this study. The aim was to collect normative data for a newly devised test including five subtests: famous face naming, fame judgement after face presentation and after name presentation, semantic knowledge about famous people after face presentation and after name presentation. Norms were calculated taking into account demographic variables such as age, sex and education and adjusted scores were used to determine inferential cut-off scores and to compute equivalent scores. Multiple regression analyses showed that age and education influenced significantly the performance on most subtests, but sex had no effect on any of them. Scores of the subtest evaluating fame judgements after name presentation were significantly influenced only by education. The only subtest whose scores were not influenced by any demographic variable was fame judgement after face presentation.

Author name recognition in degraded journal images

NASA Astrophysics Data System (ADS)

de Bodard de la Jacopière, Aliette; Likforman-Sulem, Laurence

2006-01-01

A method for extracting names in degraded documents is presented in this article. The documents targeted are images of photocopied scientific journals from various scientific domains. Due to the degradation, there is poor OCR recognition, and pieces of other articles appear on the sides of the image. The proposed approach relies on the combination of a low-level textual analysis and an image-based analysis. The textual analysis extracts robust typographic features, while the image analysis selects image regions of interest through anchor components. We report results on the University of Washington benchmark database.

Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

PubMed

Akhondi, Saber A; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F H; Hettne, Kristina M; van Mulligen, Erik M; Kors, Jan A

2016-01-01

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents. © The Author(s) 2016. Published by Oxford University Press.

Learning of Letter Names and Sounds and Their Contribution to Word Recognition

ERIC Educational Resources Information Center

Levin, Iris; Shatil-Carmon, Sivan; Asif-Rave, Ornit

2006-01-01

This study investigated knowledge of letter names and letter sounds, their learning, and their contributions to word recognition. Of 123 preschoolers examined on letter knowledge, 65 underwent training on both letter names and letter sounds in a counterbalanced order. Prior to training, children were more advanced in associating letters with their…

Recognition of Famous Names Predicts Episodic Memory Decline in Cognitively Intact Elders

PubMed Central

Seidenberg, Michael; Kay, Christina; Woodard, John L.; Nielson, Kristy A.; Smith, J. Carson; Kandah, Cassandra; Guidotti Breting, Leslie M.; Novitski, Julia; Lancaster, Melissa; Matthews, Monica; Hantke, Nathan; Butts, Alissa; Rao, Stephen M.

2013-01-01

Objective: Semantic memory impairment is common in both Mild Cognitive Impairment (MCI) and early Alzheimer’s disease (AD), and the ability to recognize familiar people is particularly vulnerable. A time-limited temporal gradient (TG) in which well known people from decades earlier are better recalled than those learned recently is also reported in both AD and MCI. In this study, we hypothesized that the TG pattern on a famous name recognition task (FNRT) administered to cognitively intact elders would predict future episodic memory decline, and would also show a significant correlation with hippocampal volume. Methods: 78 healthy elders (ages 65-90) with normal cognition and episodic memory at baseline were administered a FNRT. Follow-up episodic memory testing 18 months later produced two groups: Declining (≥ 1 SD reduction in episodic memory) and Stable (< 1 SD). Results: The Declining group (N=27) recognized fewer recent famous names than the Stable group (N=51), while recognition for remote names was comparable. Baseline MRI volumes for both the left and right hippocampus was significantly smaller in the Declining group than the Stable group. Smaller baseline hippocampal volume was also significantly correlated with poorer performance for recent, but not remote famous names. Logistic regression analyses indicated that baseline TG performance was a significant predictor of group status (Declining versus Stable) independent of chronological age and APOE ε4 inheritance. Conclusions: Famous name recognition may serve as an early pre-clinical cognitive marker of episodic memory decline in older individuals. PMID:23688215

Category-Specific Naming and Recognition Deficits in Temporal Lobe Epilepsy Surgical Patients

PubMed Central

Drane, Daniel L.; Ojemann, George A.; Aylward, Elizabeth; Ojemann, Jeffrey G.; Johnson, L. Clark; Silbergeld, Daniel L.; Miller, John W.; Tranel, Daniel

2008-01-01

Objective Based upon Damasio's “Convergence Zone” model of semantic memory, we predicted that epilepsy surgical patients with anterior temporal lobe (TL) seizure onset would exhibit a pattern of category-specific naming and recognition deficits not observed in patients with seizures arising elsewhere. Methods We assessed epilepsy patients with unilateral seizure onset of anterior TL or other origin (n = 22), pre- or postoperatively, using a set of category-specific items and a conventional measure of visual naming (Boston Naming Test: BNT). Results Category-specific naming deficits were exhibited by patients with dominant anterior TL seizure onset/resection for famous faces and animals, while category-specific recognition deficits for these same categories were exhibited by patients with nondominant anterior TL onset/resection. Patients with other seizure onset did not exhibit category-specific deficits. Naming and recognition deficits were frequently not detected by the BNT, which samples only a limited range of stimuli. Interpretation Consistent with the “convergence zone” framework, results suggest that the nondominant anterior TL plays a major role in binding sensory information into conceptual percepts for certain stimuli, while dominant TL regions function to provide a link to verbal labels for these percepts. Although observed category-specific deficits were striking, they were often missed by the BNT, suggesting that they are more prevalent than recognized in both pre- and postsurgical epilepsy patients. Systematic investigation of these deficits could lead to more refined models of semantic memory, aid in the localization of seizures, and contribute to modifications in surgical technique and patient selection in epilepsy surgery to improve neurocognitive outcome. PMID:18206185

A modular framework for biomedical concept recognition

PubMed Central

2013-01-01

Background Concept recognition is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. The development of such solutions is typically performed in an ad-hoc manner or using general information extraction frameworks, which are not optimized for the biomedical domain and normally require the integration of complex external libraries and/or the development of custom tools. Results This article presents Neji, an open source framework optimized for biomedical concept recognition built around four key characteristics: modularity, scalability, speed, and usability. It integrates modules for biomedical natural language processing, such as sentence splitting, tokenization, lemmatization, part-of-speech tagging, chunking and dependency parsing. Concept recognition is provided through dictionary matching and machine learning with normalization methods. Neji also integrates an innovative concept tree implementation, supporting overlapped concept names and respective disambiguation techniques. The most popular input and output formats, namely Pubmed XML, IeXML, CoNLL and A1, are also supported. On top of the built-in functionalities, developers and researchers can implement new processing modules or pipelines, or use the provided command-line interface tool to build their own solutions, applying the most appropriate techniques to identify heterogeneous biomedical concepts. Neji was evaluated against three gold standard corpora with heterogeneous biomedical concepts (CRAFT, AnEM and NCBI disease corpus), achieving high performance results on named entity recognition (F1-measure for overlap matching: species 95%, cell 92%, cellular components 83%, gene and proteins 76%, chemicals 65%, biological processes and molecular functions 63%, disorders 85%, and anatomical entities 82%) and on entity normalization (F1-measure for overlap name matching and correct identifier included in the returned list of identifiers: species 88

Letter Names: Effect on Letter Saying, Spelling, and Word Recognition in Hebrew.

ERIC Educational Resources Information Center

Levin, Iris; Patel, Sigal; Margalit, Tamar; Barad, Noa

2002-01-01

Examined whether letter names, which bridge the gap between oral and written language among English speaking children, have a similar function in Hebrew. In findings from studies of Israeli kindergartners and first graders, children were found to rely on letter names in performing a number of letter saying, spelling, and word recognition tasks.…

Building a protein name dictionary from full text: a machine learning term extraction approach.

PubMed

Shi, Lei; Campagne, Fabien

2005-04-07

The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

Building a protein name dictionary from full text: a machine learning term extraction approach

PubMed Central

Shi, Lei; Campagne, Fabien

2005-01-01

Background The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. Results We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. Conclusion This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt. PMID:15817129

Complementary Hemispheric Asymmetries in Object Naming and Recognition: A Voxel-Based Correlational Study

ERIC Educational Resources Information Center

Acres, K.; Taylor, K. I.; Moss, H. E.; Stamatakis, E. A.; Tyler, L. K.

2009-01-01

Cognitive neuroscientific research proposes complementary hemispheric asymmetries in naming and recognising visual objects, with a left temporal lobe advantage for object naming and a right temporal lobe advantage for object recognition. Specifically, it has been proposed that the left inferior temporal lobe plays a mediational role linking…

Identifying interactions between chemical entities in biomedical text.

PubMed

Lamurias, Andre; Ferreira, João D; Couto, Francisco M

2014-10-23

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, \\"Identifying Interactions between Chemical Entities\\" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to state-of-the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

Identifying interactions between chemical entities in biomedical text.

PubMed

Lamurias, Andre; Ferreira, João D; Couto, Francisco M

2014-12-01

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, "Identifying Interactions between Chemical Entities" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

Is it about the self or the significance? An fMRI study of self-name recognition.

PubMed

Tacikowski, P; Brechmann, A; Marchewka, A; Jednoróg, K; Dobrowolny, M; Nowicka, A

2011-01-01

Our own name, due to its high social relevance, is supposed to have a unique status in our information processing. However, demonstrating this phenomenon empirically proves difficult as famous and unknown names, to which self-name is often compared in the studies, may differ from self-name not only in terms of the 'me vs. not-me' distinction, but also as regards their emotional content and frequency of occurrence in everyday life. In this fMRI study, apart from famous and unknown names we used the names of the most important persons in our subjects' lives. When compared to famous or unknown names recognition, self-name recognition was associated with robust activations in widely distributed bilateral network including fronto-temporal, limbic and subcortical structures, however, when compared to significant other's name, the activations were present specifically in the right inferior frontal gyrus. In addition, the significant other's name produced a similar pattern of activations to the one activated by self-name. These results suggest that the differences between own and other's name processing may rather be quantitative than qualitative in nature.

[The effects of normal aging on face naming and recognition of famous people: battery 75].

PubMed

Pluchon, C; Simonnet, E; Toullat, G; Gil, R

2002-07-01

The difficulty to recall proper nouns is often something elderly people complain about. Thus, we tried to build and standardize a tool that could allow a quantified estimation of the naming and recognition abilities about famous people faces, specifying the part of gender, age and cultural level for each kind of test. The performances of 542 subjects divided in 3 age brackets and 3 academic knowledge levels were analysed. To carry out the test material, the artistic team of the Grevin Museum (Paris) was called upon. Their work offers a homogeneous way to shape famous people faces. One same person thus photographed 75 characters from different social categories with the same conditions of light, during only one day. The results of the study show that men perform better than women as concerns naming task, but that there's no difference between genders as concerns recognition task. Recognition performances are significantly better whatever the age, the gender and the cultural level may be. Generally, performances are all the more better since subjects are younger and have a higher cultural level. Our study then confirms the fact that normal aging goes hand in hand with rising difficulties to name faces. Moreover, results tend to show that recognition of faces remains better preserved and that the greater disability to recall a name is linked to difficulties in lexical accessing.

Recognition of a person named entity from the text written in a natural language

NASA Astrophysics Data System (ADS)

Dolbin, A. V.; Rozaliev, V. L.; Orlova, Y. A.

2017-01-01

This work is devoted to the semantic analysis of texts, which were written in a natural language. The main goal of the research was to compare latent Dirichlet allocation and latent semantic analysis to identify elements of the human appearance in the text. The completeness of information retrieval was chosen as the efficiency criteria for methods comparison. However, it was insufficient to choose only one method for achieving high recognition rates. Thus, additional methods were used for finding references to the personality in the text. All these methods are based on the created information model, which represents person’s appearance.

The Role of Sensory-Motor Information in Object Recognition: Evidence from Category-Specific Visual Agnosia

ERIC Educational Resources Information Center

Wolk, D.A.; Coslett, H.B.; Glosser, G.

2005-01-01

The role of sensory-motor representations in object recognition was investigated in experiments involving AD, a patient with mild visual agnosia who was impaired in the recognition of visually presented living as compared to non-living entities. AD named visually presented items for which sensory-motor information was available significantly more…

Better object recognition and naming outcome with MRI-guided stereotactic laser amygdalohippocampotomy for temporal lobe epilepsy.

PubMed

Drane, Daniel L; Loring, David W; Voets, Natalie L; Price, Michele; Ojemann, Jeffrey G; Willie, Jon T; Saindane, Amit M; Phatak, Vaishali; Ivanisevic, Mirjana; Millis, Scott; Helmers, Sandra L; Miller, John W; Meador, Kimford J; Gross, Robert E

2015-01-01

Patients with temporal lobe epilepsy (TLE) experience significant deficits in category-related object recognition and naming following standard surgical approaches. These deficits may result from a decoupling of core processing modules (e.g., language, visual processing, and semantic memory), due to "collateral damage" to temporal regions outside the hippocampus following open surgical approaches. We predicted that stereotactic laser amygdalohippocampotomy (SLAH) would minimize such deficits because it preserves white matter pathways and neocortical regions that are critical for these cognitive processes. Tests of naming and recognition of common nouns (Boston Naming Test) and famous persons were compared with nonparametric analyses using exact tests between a group of 19 patients with medically intractable mesial TLE undergoing SLAH (10 dominant, 9 nondominant), and a comparable series of TLE patients undergoing standard surgical approaches (n=39) using a prospective, nonrandomized, nonblinded, parallel-group design. Performance declines were significantly greater for the patients with dominant TLE who were undergoing open resection versus SLAH for naming famous faces and common nouns (F=24.3, p<0.0001, η2=0.57, and F=11.2, p<0.001, η2=0.39, respectively), and for the patients with nondominant TLE undergoing open resection versus SLAH for recognizing famous faces (F=3.9, p<0.02, η2=0.19). When examined on an individual subject basis, no SLAH patients experienced any performance declines on these measures. In contrast, 32 of the 39 patients undergoing standard surgical approaches declined on one or more measures for both object types (p<0.001, Fisher's exact test). Twenty-one of 22 left (dominant) TLE patients declined on one or both naming tasks after open resection, while 11 of 17 right (nondominant) TLE patients declined on face recognition. Preliminary results suggest (1) naming and recognition functions can be spared in TLE patients undergoing SLAH, and (2

Better Object Recognition and Naming Outcome With MRI-Guided Stereotactic Laser Amygdalohippocampotomy for Temporal Lobe Epilepsy

PubMed Central

Drane, Daniel L.; Loring, David W.; Voets, Natalie L.; Price, Michele; Ojemann, Jeffrey G.; Willie, Jon T.; Saindane, Amit M.; Phatak, Vaishali; Ivanisevic, Mirjana; Millis, Scott; Helmers, Sandra L.; Miller, John W.; Meador, Kimford J.; Gross, Robert E.

2015-01-01

SUMMARY OBJECTIVES Temporal lobe epilepsy (TLE) patients experience significant deficits in category-related object recognition and naming following standard surgical approaches. These deficits may result from a decoupling of core processing modules (e.g., language, visual processing, semantic memory), due to “collateral damage” to temporal regions outside the hippocampus following open surgical approaches. We predicted stereotactic laser amygdalohippocampotomy (SLAH) would minimize such deficits because it preserves white matter pathways and neocortical regions critical for these cognitive processes. METHODS Tests of naming and recognition of common nouns (Boston Naming Test) and famous persons were compared with nonparametric analyses using exact tests between a group of nineteen patients with medically-intractable mesial TLE undergoing SLAH (10 dominant, 9 nondominant), and a comparable series of TLE patients undergoing standard surgical approaches (n=39) using a prospective, non-randomized, non-blinded, parallel group design. RESULTS Performance declines were significantly greater for the dominant TLE patients undergoing open resection versus SLAH for naming famous faces and common nouns (F=24.3, p<.0001, η2=.57, & F=11.2, p<.001, η2=.39, respectively), and for the nondominant TLE patients undergoing open resection versus SLAH for recognizing famous faces (F=3.9, p<.02, η2=.19). When examined on an individual subject basis, no SLAH patients experienced any performance declines on these measures. In contrast, 32 of the 39 undergoing standard surgical approaches declined on one or more measures for both object types (p<.001, Fisher’s exact test). Twenty-one of 22 left (dominant) TLE patients declined on one or both naming tasks after open resection, while 11 of 17 right (non-dominant) TLE patients declined on face recognition. SIGNIFICANCE Preliminary results suggest 1) naming and recognition functions can be spared in TLE patients undergoing SLAH, and 2

CheNER: a tool for the identification of chemical entities and their classes in biomedical literature.

PubMed

Usié, Anabel; Cruz, Joaquim; Comas, Jorge; Solsona, Francesc; Alves, Rui

2015-01-01

Small chemical molecules regulate biological processes at the molecular level. Those molecules are often involved in causing or treating pathological states. Automatically identifying such molecules in biomedical text is difficult due to both, the diverse morphology of chemical names and the alternative types of nomenclature that are simultaneously used to describe them. To address these issues, the last BioCreAtIvE challenge proposed a CHEMDNER task, which is a Named Entity Recognition (NER) challenge that aims at labelling different types of chemical names in biomedical text. To address this challenge we tested various approaches to recognizing chemical entities in biomedical documents. These approaches range from linear Conditional Random Fields (CRFs) to a combination of CRFs with regular expression and dictionary matching, followed by a post-processing step to tag those chemical names in a corpus of Medline abstracts. We named our best performing systems CheNER. We evaluate the performance of the various approaches using the F-score statistics. Higher F-scores indicate better performance. The highest F-score we obtain in identifying unique chemical entities is 72.88%. The highest F-score we obtain in identifying all chemical entities is 73.07%. We also evaluate the F-Score of combining our system with ChemSpot, and find an increase from 72.88% to 73.83%. CheNER presents a valid alternative for automated annotation of chemical entities in biomedical documents. In addition, CheNER may be used to derive new features to train newer methods for tagging chemical entities. CheNER can be downloaded from http://metres.udl.cat and included in text annotation pipelines.

A stacked sequential learning method for investigator name recognition from web-based medical articles

NASA Astrophysics Data System (ADS)

Zhang, Xiaoli; Zou, Jie; Le, Daniel X.; Thoma, George

2010-01-01

"Investigator Names" is a newly required field in MEDLINE citations. It consists of personal names listed as members of corporate organizations in an article. Extracting investigator names automatically is necessary because of the increasing volume of articles reporting collaborative biomedical research in which a large number of investigators participate. In this paper, we present an SVM-based stacked sequential learning method in a novel application - recognizing named entities such as the first and last names of investigators from online medical journal articles. Stacked sequential learning is a meta-learning algorithm which can boost any base learner. It exploits contextual information by adding the predicted labels of the surrounding tokens as features. We apply this method to tag words in text paragraphs containing investigator names, and demonstrate that stacked sequential learning improves the performance of a nonsequential base learner such as an SVM classifier.

Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods.

PubMed

Chen, Xiaoyi; Deldossi, Myrtille; Aboukhamis, Rim; Faviez, Carole; Dahamna, Badisse; Karapetiantz, Pierre; Guenegou-Arnoux, Armelle; Girardeau, Yannick; Guillemin-Lanne, Sylvie; Lillo-Le-Louët, Agnès; Texier, Nathalie; Burgun, Anita; Katsahian, Sandrine

2017-01-01

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary source to current pharmacovigilance systems. However, the performance of text mining tools applied to social media text data to discover ADRs needs to be evaluated. In this paper, we introduce the approach developed to mine ADR from French social media. A protocol of evaluation is highlighted, which includes a detailed sample size determination and evaluation corpus constitution. Our text mining approach provided very encouraging preliminary results with F-measures of 0.94 and 0.81 for recognition of drugs and symptoms respectively, and with F-measure of 0.70 for ADR detection. Therefore, this approach is promising for downstream pharmacovigilance analysis.

Crowded and Sparse Domains in Object Recognition: Consequences for Categorization and Naming

ERIC Educational Resources Information Center

Gale, Tim M.; Laws, Keith R.; Foley, Kerry

2006-01-01

Some models of object recognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize…

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

PubMed

Jiang, Min; Chen, Yukun; Liu, Mei; Rosenbloom, S Trent; Mani, Subramani; Denny, Joshua C; Xu, Hua

2011-01-01

The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

High-recall protein entity recognition using a dictionary

PubMed Central

Kou, Zhenzhen; Cohen, William W.; Murphy, Robert F.

2010-01-01

Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches to that of Maximum Entropy (Max-Ent) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary—the measure of most interest in our intended application. PMID:15961466

Name recognition in autism: EEG evidence of altered patterns of brain activity and connectivity.

PubMed

Nowicka, Anna; Cygan, Hanna B; Tacikowski, Paweł; Ostaszewski, Paweł; Kuś, Rafał

2016-01-01

Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder (ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we investigated brain activity and functional connectivity associated with recognition of names in the high-functioning ASD group and in the control group. EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization (ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented visually: one's own, close-other's, famous, and unknown. Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals with ASD, P300 (a positive ERP component) to own-name and to a close-other's name were similar whereas in control participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range frequencies. Moreover, DTF indicated the directionality of those impaired connections-they were going from parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker connections within the frontal region and stronger connections within the occipital region in the ASD group in comparison to the control group. Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional network during recognition of visually presented names in individuals with ASD.

Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study.

PubMed

Skeppstedt, Maria; Kvist, Maria; Nilsson, Gunnar H; Dalianis, Hercules

2014-06-01

Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder+Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that

Recognition of student names past: a longitudinal study with N = 1.

PubMed

Huang, I N

1997-01-01

Recognition of names of former students taught at different times by a middle-aged college professor was tested, to investigate recognition memory over a time span ranging from 6 months to 26.5 years. The relationship between the d', a measure of strength of memory, and the retention interval can be best described by a logarithmic function characterized by a rapid initial drop followed by a slow forgetting rate. The correct responses (hits and rejections) had higher confidence and shorter response time than did the incorrect responses (false alarms and misses). The results show that an ecologically realistic longitudinal study with N = 1 can provide a valuable means in the study of human memory with very long retention intervals, which have not yet been investigated in the laboratory.

AutoMap User’s Guide

DTIC Science & Technology

2006-10-01

Hierarchy of Pre-Processing Techniques 3. NLP (Natural Language Processing) Utilities 3.1 Named-Entity Recognition 3.1.1 Example for Named-Entity... Recognition 3.2 Symbol RemovalN-Gram Identification: Bi-Grams 4. Stemming 4.1 Stemming Example 5. Delete List 5.1 Open a Delete List 5.1.1 Small...iterative and involves several key processes: • Named-Entity Recognition Named-Entity Recognition is an Automap feature that allows you to

Cell line name recognition in support of the identification of synthetic lethality in cancer from text

PubMed Central

Kaewphan, Suwisa; Van Landeghem, Sofie; Ohta, Tomoko; Van de Peer, Yves; Ginter, Filip; Pyysalo, Sampo

2016-01-01

Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus. Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers. Availability and implementation: The manually annotated datasets, the cell line dictionary, derived corpora, NERsuite models and the results of the large-scale run on unannotated texts are available under open licenses at http://turkunlp.github.io/Cell-line-recognition/. Contact: sukaew@utu.fi PMID:26428294

Crowded and sparse domains in object recognition: consequences for categorization and naming.

PubMed

Gale, Tim M; Laws, Keith R; Foley, Kerry

2006-03-01

Some models of object recognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize the same matched stimuli: two examine superordinate categorization and two examine picture naming. Experiments 1 and 2 required participants to sort pictures into their appropriate superordinate categories and both revealed faster categorization for living than nonliving things. Nonetheless, the living thing superiority disappeared when the atypical categories of body parts and musical instruments were excluded. Experiment 3 examined naming latency and found no difference between living and nonliving things. This finding was replicated in Experiment 4 where the same items were presented in different formats (e.g., color and line-drawn versions). Taken as a whole, these experiments show that the ease with which people categorize items maps strongly onto the ease with which they name them.

Spaced-retrieval effects on name-face recognition in older adults with probable Alzheimer's disease.

PubMed

Hawley, Karri S; Cherry, Katie E

2004-03-01

Six older adults with probable Alzheimer's disease (AD) were trained to recall a name-face association using the spaced-retrieval method. We administered six training sessions over a 2-week period. On each trial, participants selected a target photograph and stated the target name, from eight other photographs, at increasingly longer retention intervals. Results yielded a positive effect of spaced-retrieval training for name-face recognition. All participants were able to select the target photograph and state the target's name for longer periods of time within and across training sessions. A live-person transfer task was administered to determine whether the name-face association, trained by spaced-retrieval, would transfer to a live person. Half of the participants were able to call the live person by the correct name. These data provide initial evidence that spaced-retrieval training can aid older adults with probable AD in recall of a name-face association and in transfer of that association to an actual person.

76 FR 28503 - Identification of Three Entities as Government of Libya Entities Pursuant to Executive Order 13566

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-17

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Identification of Three Entities as Government of Libya Entities Pursuant to Executive Order 13566 AGENCY: Department of the Treasury. ACTION... names of three entities identified on May 5, 2011 as persons whose property and interests in property...

17 CFR 229.1107 - (Item 1107) Issuing entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false (Item 1107) Issuing entities....1107 (Item 1107) Issuing entities. Provide the following information about the issuing entity: (a) State the issuing entity's name and describe the issuing entity's form of organization, including the...

17 CFR 229.1107 - (Item 1107) Issuing entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false (Item 1107) Issuing entities....1107 (Item 1107) Issuing entities. Provide the following information about the issuing entity: (a) State the issuing entity's name and describe the issuing entity's form of organization, including the...

Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature.

PubMed

García-Remesal, Miguel; García-Ruiz, Alejandro; Pérez-Rey, David; de la Iglesia, Diana; Maojo, Víctor

2013-01-01

Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a "proof of concept" that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

Moving Hands, Moving Entities

ERIC Educational Resources Information Center

Setti, Annalisa; Borghi, Anna M.; Tessari, Alessia

2009-01-01

In this study we investigated with a priming paradigm whether uni and bimanual actions presented as primes differently affected language processing. Animals' (self-moving entities) and plants' (not self-moving entities) names were used as targets. As prime we used grasping hands, presented both as static images and videos. The results showed an…

Face shape and face identity processing in behavioral variant fronto-temporal dementia: A specific deficit for familiarity and name recognition of famous faces.

PubMed

De Winter, François-Laurent; Timmers, Dorien; de Gelder, Beatrice; Van Orshoven, Marc; Vieren, Marleen; Bouckaert, Miriam; Cypers, Gert; Caekebeke, Jo; Van de Vliet, Laura; Goffin, Karolien; Van Laere, Koen; Sunaert, Stefan; Vandenberghe, Rik; Vandenbulcke, Mathieu; Van den Stock, Jan

2016-01-01

Deficits in face processing have been described in the behavioral variant of fronto-temporal dementia (bvFTD), primarily regarding the recognition of facial expressions. Less is known about face shape and face identity processing. Here we used a hierarchical strategy targeting face shape and face identity recognition in bvFTD and matched healthy controls. Participants performed 3 psychophysical experiments targeting face shape detection (Experiment 1), unfamiliar face identity matching (Experiment 2), familiarity categorization and famous face-name matching (Experiment 3). The results revealed group differences only in Experiment 3, with a deficit in the bvFTD group for both familiarity categorization and famous face-name matching. Voxel-based morphometry regression analyses in the bvFTD group revealed an association between grey matter volume of the left ventral anterior temporal lobe and familiarity recognition, while face-name matching correlated with grey matter volume of the bilateral ventral anterior temporal lobes. Subsequently, we quantified familiarity-specific and name-specific recognition deficits as the sum of the celebrities of which respectively only the name or only the familiarity was accurately recognized. Both indices were associated with grey matter volume of the bilateral anterior temporal cortices. These findings extent previous results by documenting the involvement of the left anterior temporal lobe (ATL) in familiarity detection and the right ATL in name recognition deficits in fronto-temporal lobar degeneration.

Moving beyond the Name: Defining Corporate Entities to Support Provenance-Based Access

ERIC Educational Resources Information Center

Light, Michelle

2007-01-01

The second edition of the "International Standard Archival Authority Records for Corporate Bodies, Persons, and Families (ISAAR(CPF)2)" focuses on describing entities as they exist in reality, rather than on establishing authorized terms. This change allows authority records to include multiple authorized terms representing an entity as it changed…

77 FR 61658 - Designation of Two Entities Pursuant to Executive Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-10

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Designation of Two Entities Pursuant... Treasury Department's Office of Foreign Assets Control (``OFAC'') is publishing the names of two entities....'' DATES: The designation by the Director of OFAC of the two entities named in this notice, pursuant to...

A new face of sleep: The impact of post-learning sleep on recognition memory for face-name associations

PubMed Central

Maurer, Leonie; Zitting, Kirsi-Marja; Elliott, Kieran; Czeisler, Charles A.; Ronda, Joseph M.; Duffy, Jeanne F.

2015-01-01

Sleep has been demonstrated to improve consolidation of many types of new memories. However, few prior studies have examined how sleep impacts learning of face-name associations. The recognition of a new face along with the associated name is an important human cognitive skill. Here we investigated whether post-presentation sleep impacts recognition memory of new face-name associations in healthy adults. Fourteen participants were tested twice. Each time, they were presented 20 photos of faces with a corresponding name. Twelve hours later, they were shown each face twice, once with the correct and once with an incorrect name, and asked if each face-name combination was correct and to rate their confidence. In one condition the 12-hour interval between presentation and recall included an 8-hour nighttime sleep opportunity (“Sleep”), while in the other condition they remained awake (“Wake”). There were more correct and highly confident correct responses when the interval between presentation and recall included a sleep opportunity, although improvement between the “Wake” and “Sleep” conditions was not related to duration of sleep or any sleep stage. These data suggest that a nighttime sleep opportunity improves the ability to correctly recognize face-name associations. Further studies investigating the mechanism of this improvement are important, as this finding has implications for individuals with sleep disturbances and/or memory impairments. PMID:26549626

A new face of sleep: The impact of post-learning sleep on recognition memory for face-name associations.

PubMed

Maurer, Leonie; Zitting, Kirsi-Marja; Elliott, Kieran; Czeisler, Charles A; Ronda, Joseph M; Duffy, Jeanne F

2015-12-01

Sleep has been demonstrated to improve consolidation of many types of new memories. However, few prior studies have examined how sleep impacts learning of face-name associations. The recognition of a new face along with the associated name is an important human cognitive skill. Here we investigated whether post-presentation sleep impacts recognition memory of new face-name associations in healthy adults. Fourteen participants were tested twice. Each time, they were presented 20 photos of faces with a corresponding name. Twelve hours later, they were shown each face twice, once with the correct and once with an incorrect name, and asked if each face-name combination was correct and to rate their confidence. In one condition the 12-h interval between presentation and recall included an 8-h nighttime sleep opportunity ("Sleep"), while in the other condition they remained awake ("Wake"). There were more correct and highly confident correct responses when the interval between presentation and recall included a sleep opportunity, although improvement between the "Wake" and "Sleep" conditions was not related to duration of sleep or any sleep stage. These data suggest that a nighttime sleep opportunity improves the ability to correctly recognize face-name associations. Further studies investigating the mechanism of this improvement are important, as this finding has implications for individuals with sleep disturbances and/or memory impairments. Copyright © 2015 Elsevier Inc. All rights reserved.

Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection

PubMed Central

Lossio-Ventura, Juan Antonio; Hogan, William; Modave, François; Hicks, Amanda; Hanna, Josh; Guo, Yi; He, Zhe; Bian, Jiang

2017-01-01

Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesity-cancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993. PMID:28503356

Encoding of Fundamental Chemical Entities of Organic Reactivity Interest using chemical ontology and XML.

PubMed

Durairaj, Vijayasarathi; Punnaivanam, Sankar

2015-09-01

Fundamental chemical entities are identified in the context of organic reactivity and classified as appropriate concept classes namely ElectronEntity, AtomEntity, AtomGroupEntity, FunctionalGroupEntity and MolecularEntity. The entity classes and their subclasses are organized into a chemical ontology named "ChemEnt" for the purpose of assertion, restriction and modification of properties through entity relations. Individual instances of entity classes are defined and encoded as a library of chemical entities in XML. The instances of entity classes are distinguished with a unique notation and identification values in order to map them with the ontology definitions. A model GUI named Entity Table is created to view graphical representations of all the entity instances. The detection of chemical entities in chemical structures is achieved through suitable algorithms. The possibility of asserting properties to the entities at different levels and the mechanism of property flow within the hierarchical entity levels is outlined. Copyright © 2015 Elsevier Inc. All rights reserved.

Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

PubMed

Mrabet, Yassine; Kilicoglu, Halil; Roberts, Kirk; Demner-Fushman, Dina

2016-01-01

Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-domain knowledge bases. In the first step of our method, we recognize named entities in consumer health questions using an unsupervised method that relies on a biomedical knowledge base, UMLS, and an open-domain knowledge base, DBpedia. In the next step, we cast topic recognition as a binary classification problem of deciding whether a named entity is the question topic or not. We evaluated our approach on a dataset from the National Library of Medicine (NLM), introduced in this paper, and another from the Genetic and Rare Disease Information Center (GARD). The combination of knowledge bases outperformed the results obtained by individual knowledge bases by up to 16.5% F1 and achieved state-of-the-art performance. Our results demonstrate that combining open-domain knowledge bases with biomedical knowledge bases can lead to a substantial improvement in understanding user-generated health content.

Context and Domain Knowledge Enhanced Entity Spotting in Informal Text

NASA Astrophysics Data System (ADS)

Gruhl, Daniel; Nagarajan, Meena; Pieper, Jan; Robson, Christine; Sheth, Amit

This paper explores the application of restricted relationship graphs (RDF) and statistical NLP techniques to improve named entity annotation in challenging Informal English domains. We validate our approach using on-line forums discussing popular music. Named entity annotation is particularly difficult in this domain because it is characterized by a large number of ambiguous entities, such as the Madonna album "Music" or Lilly Allen's pop hit "Smile".

Name Writing but not Environmental Print Recognition Is Related to Letter-Sound Knowledge and Phonological Awareness in Pre-Readers

ERIC Educational Resources Information Center

Blair, Rebecca; Savage, Robert

2006-01-01

This paper reports a study exploring the associations between measures of two levels of phonological representation: recognition (epi-linguistic) and production (meta-linguistic) tasks, and very early reading and writing skills. Thirty-eight pre-reading Ottawa-area children, aged 4-5 years, named environmental print (EP), wrote their own name,…

Detection of IUPAC and IUPAC-like chemical names.

PubMed

Klinger, Roman; Kolárik, Corinna; Fluck, Juliane; Hofmann-Apitius, Martin; Friedrich, Christoph M

2008-07-01

Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. We present an IUPAC name recognizer with an F(1) measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F(1) measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component.

The contribution of discrete-trial naming and visual recognition to rapid automatized naming deficits of dyslexic children with and without a history of language delay

PubMed Central

Gasperini, Filippo; Brizzolara, Daniela; Cristofani, Paola; Casalini, Claudia; Chilosi, Anna Maria

2014-01-01

Children with Developmental Dyslexia (DD) are impaired in Rapid Automatized Naming (RAN) tasks, where subjects are asked to name arrays of high frequency items as quickly as possible. However the reasons why RAN speed discriminates DD from typical readers are not yet fully understood. Our study was aimed to identify some of the cognitive mechanisms underlying RAN-reading relationship by comparing one group of 32 children with DD with an age-matched control group of typical readers on a naming and a visual recognition task both using a discrete-trial methodology, in addition to a serial RAN task, all using the same stimuli (digits and colors). Results showed a significant slowness of DD children in both serial and discrete-trial naming (DN) tasks regardless of type of stimulus, but no difference between the two groups on the discrete-trial recognition task. Significant differences between DD and control participants in the RAN task disappeared when performance in the DN task was partialled out by covariance analysis for colors, but not for digits. The same pattern held in a subgroup of DD subjects with a history of early language delay (LD). By contrast, in a subsample of DD children without LD the RAN deficit was specific for digits and disappeared after slowness in DN was partialled out. Slowness in DN was more evident for LD than for noLD DD children. Overall, our results confirm previous evidence indicating a name-retrieval deficit as a cognitive impairment underlying RAN slowness in DD children. This deficit seems to be more marked in DD children with previous LD. Moreover, additional cognitive deficits specifically associated with serial RAN tasks have to be taken into account when explaining deficient RAN speed of these latter children. We suggest that partially different cognitive dysfunctions underpin superficially similar RAN impairments in different subgroups of DD subjects. PMID:25237301

Assessment of Orthographic Similarity of Drugs Names between Iran and Overseas Using the Solar Model

PubMed Central

ABOLHASSANI, Nazanin; AKBARI SARI, Ali; RASHIDIAN, Arash; RASTEGARPANAH, Mansoor

2017-01-01

Background: The recognition of patient safety is now occupying a prominent place on the health policy agenda since medical errors can result in adverse events. The existence of confusing drug names is one of the most common causes of medication errors. In Iran, the General Office of Trademarks Registry (GOTR), for four years (2010–2014) was responsible for approving drug proprietary names. This study aimed to investigate the performance of the GOTR in terms of drug names orthographic similarity using the SOLAR model. Methods: First, 100 names were randomly selected from the GOTR’s database. Then, each name was searched through pharmaceutical websites including Martindale (the Complete Drug Reference published by Pharmaceutical Press), Drugs.com and Medicines Complete. Pair of drugs whose names look orthographically similar with different indications were identified. Then, the SOLAR model was utilized to determine orthographic similarity between all pair of drug names. Results: The mean of match values of these 100 pairs of drug was 77% indicating the high risk of similarity. The match value for most of the reviewed pairs (92%) was high (≥66%). This value was medium (≥ 33% and <66%) just for 8% of the pairs of drug. These results indicate high risk of confusion due to similarity of drug names. Conclusion: The stewardship of the GOTR in patient safety considerations is fundamentally problematic. Thus, as a best practice, we recommend that proprietary names of drugs be evaluated by an entity within the health system. While an entity within the health system should address patient safety considerations, the GOTR is responsible for intellectual property rights. PMID:29259940

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 2 2011-07-01 2011-07-01 false Political entities and public... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false Political entities and public... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

Detection of IUPAC and IUPAC-like chemical names

PubMed Central

Klinger, Roman; Kolářik, Corinna; Fluck, Juliane; Hofmann-Apitius, Martin; Friedrich, Christoph M.

2008-01-01

Motivation: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. Results: We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. Availability: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component. Contact: roman.klinger@scai.fraunhofer.de PMID:18586724

Noun and knowledge retrieval for biological and non-biological entities following right occipitotemporal lesions.

PubMed

Bruffaerts, Rose; De Weer, An-Sofie; De Grauwe, Sophie; Thys, Miek; Dries, Eva; Thijs, Vincent; Sunaert, Stefan; Vandenbulcke, Mathieu; De Deyne, Simon; Storms, Gerrit; Vandenberghe, Rik

2014-09-01

We investigated the critical contribution of right ventral occipitotemporal cortex to knowledge of visual and functional-associative attributes of biological and non-biological entities and how this relates to category-specificity during confrontation naming. In a consecutive series of 7 patients with lesions confined to right ventral occipitotemporal cortex, we conducted an extensive assessment of oral generation of visual-sensory and functional-associative features in response to the names of biological and nonbiological entities. Subjects also performed a confrontation naming task for these categories. Our main novel finding related to a unique case with a small lesion confined to right medial fusiform gyrus who showed disproportionate naming impairment for nonbiological versus biological entities, specifically for tools. Generation of visual and functional-associative features was preserved for biological and non-biological entities. In two other cases, who had a relatively small posterior lesion restricted to primary visual and posterior fusiform cortex, retrieval of visual attributes was disproportionately impaired compared to functional-associative attributes, in particular for biological entities. However, these cases did not show a category-specific naming deficit. Two final cases with the largest lesions showed a classical dissociation between biological versus nonbiological entities during naming, with normal feature generation performance. This is the first lesion-based evidence of a critical contribution of the right medial fusiform cortex to tool naming. Second, dissociations along the dimension of attribute type during feature generation do not co-occur with category-specificity during naming in the current patient sample. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adapting Web content for low-literacy readers by using lexical elaboration and named entities labeling

NASA Astrophysics Data System (ADS)

Watanabe, W. M.; Candido, A.; Amâncio, M. A.; De Oliveira, M.; Pardo, T. A. S.; Fortes, R. P. M.; Aluísio, S. M.

2010-12-01

This paper presents an approach for assisting low-literacy readers in accessing Web online information. The "Educational FACILITA" tool is a Web content adaptation tool that provides innovative features and follows more intuitive interaction models regarding accessibility concerns. Especially, we propose an interaction model and a Web application that explore the natural language processing tasks of lexical elaboration and named entity labeling for improving Web accessibility. We report on the results obtained from a pilot study on usability analysis carried out with low-literacy users. The preliminary results show that "Educational FACILITA" improves the comprehension of text elements, although the assistance mechanisms might also confuse users when word sense ambiguity is introduced, by gathering, for a complex word, a list of synonyms with multiple meanings. This fact evokes a future solution in which the correct sense for a complex word in a sentence is identified, solving this pervasive characteristic of natural languages. The pilot study also identified that experienced computer users find the tool to be more useful than novice computer users do.

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 2 Grants and Agreements 1 2014-01-01 2014-01-01 false Types of entities to which this part applies... or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1) None of... her name). (2) None of the requirements regarding reporting names and total compensation of an entity...

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 2 Grants and Agreements 1 2011-01-01 2011-01-01 false Types of entities to which this part applies... or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1) None of... her name). (2) None of the requirements regarding reporting names and total compensation of an entity...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... corporations. 306.88 Section 306.88 Money and Finance: Treasury Regulations Relating to Money and Finance... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... corporations. 306.88 Section 306.88 Money and Finance: Treasury Regulations Relating to Money and Finance... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... corporations. 306.88 Section 306.88 Money and Finance: Treasury Regulations Relating to Money and Finance... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

76 FR 52384 - Designation of Additional Entities Pursuant to Executive Order 13405

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-22

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Designation of Additional Entities... Assets Control (``OFAC'') is publishing the names of four newly-designated entities whose property and... the Director of OFAC of the four entities identified in this notice, pursuant to Executive [[Page...

Proper name retrieval in temporal lobe epilepsy: naming of famous faces and landmarks.

PubMed

Benke, Thomas; Kuen, Eva; Schwarz, Michael; Walser, Gerald

2013-05-01

The objective of this study was to further explore proper name (PN) retrieval and conceptual knowledge in patients with left and right temporal lobe epilepsy (69 patients with LTLE and 62 patients with RTLE) using a refined assessment procedure. Based on the performance of a large group of age- and education-matched normals, a new test of famous faces and famous landmarks was designed. Recognition, naming, and semantic knowledge were assessed consecutively, allowing for a better characterization of deficient levels in the naming system. Impairment in PN retrieval was common in the cohort with TLE. Furthermore, side of seizure onset impaired stages of name retrieval differently: LTLE impaired the lexico-phonological processing, whereas RTLE mainly impaired the perceptual-semantic stage of object recognition. In addition to deficient PN retrieval, patients with TLE had reduced conceptual knowledge regarding famous persons and landmarks. Copyright © 2013 Elsevier Inc. All rights reserved.

Automatic Recognition of Object Names in Literature

NASA Astrophysics Data System (ADS)

Bonnin, C.; Lesteven, S.; Derriere, S.; Oberto, A.

2008-08-01

SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

The left temporal pole is a heteromodal hub for retrieving proper names

PubMed Central

Waldron, Eric J.; Manzel, Kenneth; Tranel, Daniel

2015-01-01

The left temporal pole (LTP) has been posited to be a heteromodal hub for retrieving proper names for semantically unique entities. Previous investigations have demonstrated that LTP is important for retrieving names for famous faces and unique landmarks. However, whether such a relationship would hold for unique entities apprehended through stimulus modalities other than vision has not been well established, and such evidence is critical to adjudicate claims about the “heteromodal” nature of the LTP. Here, we tested the hypothesis that the LTP would be important for naming famous voices. Individuals with LTP lesions were asked to recognize and name famous persons speaking in audio clips. Relative to neurologically normal and brain damaged comparison participants, patients with LTP lesions were able to recognize famous persons from their voices normally, but were selectively impaired in naming famous persons from their voices. The current results extend previous research and provide further support for the notion that the LTP is a convergence region serving as a heteromodal hub for retrieving the names of semantically unique entities. PMID:24389260

77 FR 31806 - Changes to Implement Micro Entity Status for Paying Patent Fees

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-30

... legislative history of 35 U.S.C. 123 is clear that it is directed to a subset of small entities, namely... history do not, for example, contemplate a for-profit, large entity applicant becoming a ``micro entity... across government agencies and identified goals designed to promote innovation; (8) considered approaches...

Recognizing the Emotional Valence of Names: An ERP Study

ERIC Educational Resources Information Center

Wang, Lin; Zhu, Zude; Bastiaansen, Marcel; Hagoort, Peter; Yang, Yufang

2013-01-01

Unlike common nouns, person names refer to unique entities and generally have a referring function. We used event-related potentials to investigate the time course of identifying the emotional meaning of nouns and names. The emotional valence of names and nouns were manipulated separately. The results show early N1 effects in response to emotional…

Face-Name Association Learning and Brain Structural Substrates in Alcoholism

PubMed Central

Pitel, Anne-Lise; Chanraud, Sandra; Rohlfing, Torsten; Pfefferbaum, Adolf; Sullivan, Edith V.

2011-01-01

Background Associative learning is required for face-name association and is impaired in alcoholism, but the cognitive processes and brain structural components underlying this deficit remain unclear. It is also unknown whether prompting alcoholics to implement a deep level of processing during face-name encoding would enhance performance. Methods Abstinent alcoholics and controls performed a levels-of-processing face-name learning task. Participants indicated whether the face was that of an honest person (deep encoding) or that of a man (shallow encoding). Retrieval was examined using an associative (face-name) recognition task and a single-item (face or name only) recognition task. Participants also underwent a 3T structural MRI. Results Compared with controls, alcoholics had poorer associative and single-item recognition, each impaired to the same extent. Level of processing at encoding had little effect on recognition performance but affected reaction time. Correlations with brain volumes were generally modest and based primarily on reaction time in alcoholics, where the deeper the processing at encoding, the more restricted the correlations with brain volumes. In alcoholics, longer control task reaction times correlated modestly with volumes across several anterior to posterior brain regions; shallow encoding correlated with calcarine and striatal volumes; deep encoding correlated with precuneus and parietal volumes; associative recognition RT correlated with cerebellar volumes. In controls, poorer associative recognition with deep encoding correlated significantly with smaller volumes of frontal and striatal structures. Conclusions Despite prompting, alcoholics did not take advantage of encoding memoranda at a deep level to enhance face-name recognition accuracy. Nonetheless, conditions of deeper encoding resulted in faster reaction times and more specific relations with regional brain volumes than did shallow encoding. The normal relation between associative

Unstable solar lentigo: A defined separate entity.

PubMed

Byrom, Lisa; Barksdale, Sarah; Weedon, David; Muir, Jim

2016-08-01

An unstable solar lentigo is a solar lentigo with areas of melanocytic hyperplasia not extending past the margin of the lesion. They are discrete, macular, pigmented lesions arising on sun-damaged skin and a subset of typical solar lentigos. Clinically they differ from usual solar lentigines in often being solitary or larger and darker than adjacent solar lentigines. These lesions are of clinical importance as they can arise in close proximity to lentigo maligna and in a single lesion there can be demonstrated changes of solar lentigo, unstable solar lentigo and lentigo maligna. These observations led us to conjecture that unstable solar lentigos could be a precursor lesion to lentigo maligna. In this article we examine the possibility that lentigo maligna can arise within a solar lentigo through an intermediate lesion, the unstable solar lentigo. We propose that the histopathological recognition of this entity will allow for future research into its behaviour and thus management. We review difficulties in the diagnosis of single cell predominant melanocytic proliferations and the concept of unstable lentigo in view of the literature and clinical experience supporting the proposal of its recognition as a separate entity. © 2016 The Australasian College of Dermatologists.

Common neural systems associated with the recognition of famous faces and names: An event-related fMRI study

PubMed Central

Nielson, Kristy A.; Seidenberg, Michael; Woodard, John L.; Durgerian, Sally; Zhang, Qi; Gross, William L.; Gander, Amelia; Guidotti, Leslie M.; Antuono, Piero; Rao, Stephen M.

2010-01-01

Person recognition can be accomplished through several modalities (face, name, voice). Lesion, neurophysiology and neuroimaging studies have been conducted in an attempt to determine the similarities and differences in the neural networks associated with person identity via different modality inputs. The current study used event-related functional-MRI in 17 healthy participants to directly compare activation in response to randomly presented famous and non-famous names and faces (25 stimuli in each of the four categories). Findings indicated distinct areas of activation that differed for faces and names in regions typically associated with pre-semantic perceptual processes. In contrast, overlapping brain regions were activated in areas associated with the retrieval of biographical knowledge and associated social affective features. Specifically, activation for famous faces was primarily right lateralized and famous names were left lateralized. However, for both stimuli, similar areas of bilateral activity were observed in the early phases of perceptual processing. Activation for fame, irrespective of stimulus modality, activated an extensive left hemisphere network, with bilateral activity observed in the hippocampi, posterior cingulate, and middle temporal gyri. Findings are discussed within the framework of recent proposals concerning the neural network of person identification. PMID:20167415

Designing Rules for Accounting Transaction Identification based on Indonesian NLP

NASA Astrophysics Data System (ADS)

Iswandi, I.; Suwardi, I. S.; Maulidevi, N. U.

2017-03-01

Recording accounting transactions carried out by the evidence of the transactions. It can be invoices, receipts, letters of intent, electricity bill, telephone bill, etc. In this paper, we proposed design of rules to identify the entities located on the sales invoice. There are some entities identified in a sales invoice, namely : invoice date, company name, invoice number, product id, product name, quantity and total price. Identification this entities using named entity recognition method. The entities generated from the rules used as a basis for automation process of data input into the accounting system.

Familiarity or Conceptual Priming: Event-Related Potentials in Name Recognition

ERIC Educational Resources Information Center

Stenberg, Georg; Hellman, Johan; Johansson, Mikael; Rosen, Ingmar

2009-01-01

Recent interest has been drawn to the separate components of recognition memory, as studied by event-related potentials (ERPs). In ERPs, recollection is usually accompanied by a late, parietal positive deflection. An earlier, frontal component has been suggested to be a counterpart, accompanying recognition by familiarity. However, this component,…

Face-name association learning and brain structural substrates in alcoholism.

PubMed

Pitel, Anne-Lise; Chanraud, Sandra; Rohlfing, Torsten; Pfefferbaum, Adolf; Sullivan, Edith V

2012-07-01

Associative learning is required for face-name association and is impaired in alcoholism, but the cognitive processes and brain structural components underlying this deficit remain unclear. It is also unknown whether prompting alcoholics to implement a deep level of processing during face-name encoding would enhance performance. Abstinent alcoholics and controls performed a levels-of-processing face-name learning task. Participants indicated whether the face was that of an honest person (deep encoding) or that of a man (shallow encoding). Retrieval was examined using an associative (face-name) recognition task and a single-item (face or name only) recognition task. Participants also underwent 3T structural MRI. Compared with controls, alcoholics had poorer associative and single-item learning and performed at similar levels. Level of processing at encoding had little effect on recognition performance but affected reaction time (RT). Correlations with brain volumes were generally modest and based primarily on RT in alcoholics, where the deeper the processing at encoding, the more restricted the correlations with brain volumes. In alcoholics, longer control task RTs correlated modestly with smaller tissue volumes across several anterior to posterior brain regions; shallow encoding correlated with calcarine and striatal volumes; deep encoding correlated with precuneus and parietal volumes; and associative recognition RT correlated with cerebellar volumes. In controls, poorer associative recognition with deep encoding correlated significantly with smaller volumes of frontal and striatal structures. Despite prompting, alcoholics did not take advantage of encoding memoranda at a deep level to enhance face-name recognition accuracy. Nonetheless, conditions of deeper encoding resulted in faster RTs and more specific relations with regional brain volumes than did shallow encoding. The normal relation between associative recognition and corticostriatal volumes was not

How Does Using Object Names Influence Visual Recognition Memory?

ERIC Educational Resources Information Center

Richler, Jennifer J.; Palmeri, Thomas J.; Gauthier, Isabel

2013-01-01

Two recent lines of research suggest that explicitly naming objects at study influences subsequent memory for those objects at test. Lupyan (2008) suggested that naming "impairs" memory by a representational shift of stored representations of named objects toward the prototype (labeling effect). MacLeod, Gopie, Hourihan, Neary, and Ozubko (2010)…

Charting the functional relevance of Broca's area for visual word recognition and picture naming in Dutch using fMRI-guided TMS.

PubMed

Wheat, Katherine L; Cornelissen, Piers L; Sack, Alexander T; Schuhmann, Teresa; Goebel, Rainer; Blomert, Leo

2013-05-01

Magnetoencephalography (MEG) has shown pseudohomophone priming effects at Broca's area (specifically pars opercularis of left inferior frontal gyrus and precentral gyrus; LIFGpo/PCG) within ∼100ms of viewing a word. This is consistent with Broca's area involvement in fast phonological access during visual word recognition. Here we used online transcranial magnetic stimulation (TMS) to investigate whether LIFGpo/PCG is necessary for (not just correlated with) visual word recognition by ∼100ms. Pulses were delivered to individually fMRI-defined LIFGpo/PCG in Dutch speakers 75-500ms after stimulus onset during reading and picture naming. Reading and picture naming reactions times were significantly slower following pulses at 225-300ms. Contrary to predictions, there was no disruption to reading for pulses before 225ms. This does not provide evidence in favour of a functional role for LIFGpo/PCG in reading before 225ms in this case, but does extend previous findings in picture stimuli to written Dutch words. Copyright © 2012 Elsevier Inc. All rights reserved.

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2014 CFR

2014-01-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2011 CFR

2011-01-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2010 CFR

2010-07-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2013 CFR

2013-07-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2012 CFR

2012-01-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

Classifying Web Pages by Using Knowledge Bases for Entity Retrieval

NASA Astrophysics Data System (ADS)

Kiritani, Yusuke; Ma, Qiang; Yoshikawa, Masatoshi

In this paper, we propose a novel method to classify Web pages by using knowledge bases for entity search, which is a kind of typical Web search for information related to a person, location or organization. First, we map a Web page to entities according to the similarities between the page and the entities. Various methods for computing such similarity are applied. For example, we can compute the similarity between a given page and a Wikipedia article describing a certain entity. The frequency of an entity appearing in the page is another factor used in computing the similarity. Second, we construct a directed acyclic graph, named PEC graph, based on the relations among Web pages, entities, and categories, by referring to YAGO, a knowledge base built on Wikipedia and WordNet. Finally, by analyzing the PEC graph, we classify Web pages into categories. The results of some preliminary experiments validate the methods proposed in this paper.

We, the Named.

ERIC Educational Resources Information Center

Clarke, John Henrik

1989-01-01

The term "African" has gone through several phases of acceptability in the course of United States history. Changes in the applicability of the name reflect developments in African-American consciousness in the context of national and world history. Recognition of African identity is influencing Black definition and direction worldwide.…

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

PubMed Central

Rebholz-Schuhmann, Dietrich; Kim, Jee-Hyub; Yan, Ying; Dixit, Abhishek; Friteyre, Caroline; Hoehndorf, Robert; Backofen, Rolf; Lewin, Ian

2013-01-01

Motivation Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result This study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. Conclusion LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open

Playing biology's name game: identifying protein names in scientific text.

PubMed

Hanisch, Daniel; Fluck, Juliane; Mevissen, Heinz-Theodor; Zimmer, Ralf

2003-01-01

A growing body of work is devoted to the extraction of protein or gene interaction information from the scientific literature. Yet, the basis for most extraction algorithms, i.e. the specific and sensitive recognition of protein and gene names and their numerous synonyms, has not been adequately addressed. Here we describe the construction of a comprehensive general purpose name dictionary and an accompanying automatic curation procedure based on a simple token model of protein names. We designed an efficient search algorithm to analyze all abstracts in MEDLINE in a reasonable amount of time on standard computers. The parameters of our method are optimized using machine learning techniques. Used in conjunction, these ingredients lead to good search performance. A supplementary web page is available at http://cartan.gmd.de/ProMiner/.

Patient protection and Affordable Care Act; data collection to support standards related to essential health benefits; recognition of entities for the accreditation of qualified health plans. Final rule.

PubMed

2012-07-20

This final rule establishes data collection standards necessary to implement aspects of section 1302 of the Patient Protection and Affordable Care Act (Affordable Care Act), which directs the Secretary of Health and Human Services to define essential health benefits. This final rule outlines the data on applicable plans to be collected from certain issuers to support the definition of essential health benefits. This final rule also establishes a process for the recognition of accrediting entities for purposes of certification of qualified health plans.

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature

PubMed Central

Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V.; Lawson, Andrew; Zeng, Jia; Johnson, Amber M.; Holla, Vijaykumar; Bailey, Ann M.; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W.

2015-01-01

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org PMID:25858285

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature.

PubMed

Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V; Lawson, Andrew; Zeng, Jia; Johnson, Amber M; Holla, Vijaykumar; Bailey, Ann M; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W

2015-01-01

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org © The Author(s) 2015. Published by Oxford University Press.

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 2 Grants and Agreements 1 2013-01-01 2013-01-01 false Types of entities to which this part applies...) Apply for or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1... his or her name). (2) None of the requirements regarding reporting names and total compensation of an...

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 2 Grants and Agreements 1 2012-01-01 2012-01-01 false Types of entities to which this part applies...) Apply for or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1... his or her name). (2) None of the requirements regarding reporting names and total compensation of an...

By which name should I call thee? The consequences of having multiple names.

PubMed

Stevenage, Sarah V; Lewis, Hugh G

2005-11-01

The nominal competitor effect suggests that, when a person has two names associated with them, recall of either name is more difficult than if they just had one name. Drawing on a connectionist framework, this effect could arise either if multiple names were represented as being connected to a single person identity node (PIN), or if multiple names were represented as being connected via one-to-one links to multiple PINs. Whilst the latter has intuitive appeal, results from two experiments support the former architecture. Having two names connected to a single PIN not only gives rise to a nominal competitor effect (Experiment 1), but also gives rise to a familiarity enhancement effect (Experiment 2). These empirical results are simulated using an extension of Brédart, Valentine, Calder, and Gassi's (1995) connectionist architecture, which reveals that both effects hold even when the association of both names to the PIN is unequal. These results are presented in terms of a more complete model for person recognition, and the representation of semantic information within such a model is examined.

12 CFR 602.24 - Responses to demands served on non-FCA employees or entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Responses to demands served on non-FCA employees or entities. 602.24 Section 602.24 Banks and Banking FARM CREDIT ADMINISTRATION ADMINISTRATIVE... Not a Named Party § 602.24 Responses to demands served on non-FCA employees or entities. If you are...

Invention and validation of an automated camera system that uses optical character recognition to identify patient name mislabeled samples.

PubMed

Hawker, Charles D; McCarthy, William; Cleveland, David; Messinger, Bonnie L

2014-03-01

Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.

Activity Recognition in Social Media

DTIC Science & Technology

2015-12-29

AFRL-AFOSR-JP-TR-2016-0044 Activity Recognition in Social Media Subhasis Chaudhuri INDIAN INSTITUTE OF TECHNOLOGY BOMBAY Final Report 05/09/2016...DATES COVERED (From - To) 12 Aug 2013 to 30 Sep 2015 4. TITLE AND SUBTITLE Activity Recognition in Social Media 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) INDIAN INSTITUTE OF TECHNOLOGY BOMBAY POWAI MUMBAI, 400076 IN 8. PERFORMING ORGANIZATION REPORT NUMBER

31 CFR 598.408 - Alleged change in ownership or control of an entity designated as a specially designated...

Code of Federal Regulations, 2010 CFR

2010-07-01

... of capital; and contracts evidencing the sale of the entity to its new owners. (b) Any continuing... narcotics trafficker could lead to designation of the purchaser. Mere change in name of an entity will not...

What's in a Name: The Place of Recognition in a Hospitable Classroom

ERIC Educational Resources Information Center

Stratman, Jacob

2015-01-01

In this brief article, I argue that recognition is the key virtue of a hospitable classroom. Whether we are discussing the relationship between the teacher and the student, the student and other students, the student and the subject of study, or the teacher and the subject of study, recognition is the building block to a classroom that welcomes…

Naming the Ethological Subject.

PubMed

Benson, Etienne S

2016-03-01

Argument In recent decades, through the work of Jane Goodall and other ethologists, the practice of giving personal names to nonhuman animals who are the subjects of scientific research has become associated with claims about animal personhood and scientific objectivity. While critics argue that such naming practices predispose the researcher toward anthropomorphism, supporters suggest that it sensitizes the researcher to individual differences and social relations. Both critics and supporters agree that naming tends to be associated with the recognition of individual animal rights. The history of the naming of research animals since the late nineteenth century shows, however, that the practice has served a variety of purposes, most of which have raised few ethical or epistemological concerns. Names have been used to identify research animals who play dual roles as pets, workers, or patients, to enhance their market value, and to facilitate their identification in the field. The multifaceted history of naming suggests both that the use of personal names by Goodall and others is less of a radical break with previous practices than it might first appear to be and that the use of personal names to recognize the individuality, sentience, or rights of nonhuman animals faces inherent limits and contradictions.

Geographic names of the Antarctic

USGS Publications Warehouse

,; ,; ,; ,; Alberts, Fred G.

1995-01-01

This gazetteer contains 12,710 names approved by the United States Board on Geographic Names and the Secretary of the Interior for features in Antarctica and the area extending northward to the Antarctic Convergence. Included in this geographic area, the Antarctic region, are the off-lying South Shetland Islands, the South Orkney Islands, the South Sandwich Islands, South Georgia, Bouvetøya, Heard Island, and the Balleny Islands. These names have been approved for use by U.S. Government agencies. Their use by the Antarctic specialist and the public is highly recommended for the sake of accuracy and uniformity. This publication, which supersedes previous Board gazetteers or lists for the area, contains names approved as recently as December 1994. The basic name coverage of this gazetteer corresponds to that of maps at the scale of 1:250,000 or larger for coastal Antarctica, the off-lying islands, and isolated mountains and ranges of the continent. Much of the interior of Antarctica is a featureless ice plateau. That area has been mapped at a smaller scale and is nearly devoid of toponyms. All of the names are for natural features, such as mountains, glaciers, peninsulas, capes, bays, islands, and subglacial entities. The names of scientific stations have not been listed alphabetically, but they may appear in the texts of some decisions. For the names of submarine features, reference should be made to the Gazetteer of Undersea Features, 4th edition, U.S. Board on Geographic Names, 1990.

Determining similarity of scientific entities in annotation datasets

PubMed Central

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug–drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called ‘AnnSim’ that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1–1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ PMID:25725057

How brand names are special: brands, words, and hemispheres.

PubMed

Gontijo, Possidonia F D; Rayman, Janice; Zhang, Shi; Zaidel, Eran

2002-09-01

Previous research has consistently shown differences between the processing of proper names and of common nouns, leading to the belief that proper names possess a special neuropsychological status. We investigate the category of brand names and suggest that brand names also have a special neuropsychological status, but one which is different from proper names. The findings suggest that the hemispheric lexical status of the brand names is mixed--they behave like words in some respects and like nonwords in others. Our study used familiar upper case brand names, common nouns, and two different types of nonwords ("weird" and "normal") differing in length, as stimuli in a lateralized lexical decision task (LDT). Common nouns, brand names, weird nonwords, and normal nonwords were recognized in that decreasing order of speed and accuracy. A right visual field (RVF) advantage was found for all four lexical types. Interestingly, brand names, similar to nonwords, were found to be less lateralized than common nouns, consistent with theories of category-specific lexical processing. Further, brand names were the only type of lexical items to show a capitalization effect: brand names were recognized faster when they were presented in upper case than in lower case. In addition, while string length affected the recognition of common nouns only in the left visual field (LVF) and the recognition of nonwords only in the RVF, brand names behaved like common nouns in exhibiting length effects only in the LVF. Copyright 2002 Elsevier Science (USA)

University of Glasgow at TREC 2009: Experiments with Terrier

DTIC Science & Technology

2009-11-01

identify entities in the category B subset of the corpus, we resort to an efficient dictionary -based named en- tity recognition approach.4 In particular...we build a large dictio- nary of entity names using DBPedia,5 a structured representation of Wikipedia. Dictionary entries comprise all known...aliases for each unique entity, as obtained from DBPedia (e.g., ‘Barack Obama’ is represented by the dictionary entries ‘Barack Obama’ and ‘44th President

Brand name changes help health care providers win market recognition.

PubMed

Keesling, G

1993-01-01

As the healthcare industry continues to recognize the strategic implications of branding, more providers will undertake an identity change to better position themselves in competitive markets. The paper examines specific healthcare branding decisions, the reasons prompting brand name decisions and the marketing implications for a change in brand name.

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Determining similarity of scientific entities in annotation datasets.

PubMed

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug-drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called 'AnnSim' that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1-1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ © The Author(s) 2015. Published by Oxford University Press.

Feature engineering for drug name recognition in biomedical texts: feature conjunction and feature selection.

PubMed

Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming

2015-01-01

Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

Perceptual Plasticity for Auditory Object Recognition

PubMed Central

Heald, Shannon L. M.; Van Hedger, Stephen C.; Nusbaum, Howard C.

2017-01-01

In our auditory environment, we rarely experience the exact acoustic waveform twice. This is especially true for communicative signals that have meaning for listeners. In speech and music, the acoustic signal changes as a function of the talker (or instrument), speaking (or playing) rate, and room acoustics, to name a few factors. Yet, despite this acoustic variability, we are able to recognize a sentence or melody as the same across various kinds of acoustic inputs and determine meaning based on listening goals, expectations, context, and experience. The recognition process relates acoustic signals to prior experience despite variability in signal-relevant and signal-irrelevant acoustic properties, some of which could be considered as “noise” in service of a recognition goal. However, some acoustic variability, if systematic, is lawful and can be exploited by listeners to aid in recognition. Perceivable changes in systematic variability can herald a need for listeners to reorganize perception and reorient their attention to more immediately signal-relevant cues. This view is not incorporated currently in many extant theories of auditory perception, which traditionally reduce psychological or neural representations of perceptual objects and the processes that act on them to static entities. While this reduction is likely done for the sake of empirical tractability, such a reduction may seriously distort the perceptual process to be modeled. We argue that perceptual representations, as well as the processes underlying perception, are dynamically determined by an interaction between the uncertainty of the auditory signal and constraints of context. This suggests that the process of auditory recognition is highly context-dependent in that the identity of a given auditory object may be intrinsically tied to its preceding context. To argue for the flexible neural and psychological updating of sound-to-meaning mappings across speech and music, we draw upon examples

Naming, labeling, and packaging of pharmaceuticals.

PubMed

Kenagy, J W; Stein, G C

2001-11-01

The problem of medical errors associated with the naming, labeling, and packaging of pharmaceuticals is discussed. Sound-alike and look-alike drug names and packages can lead pharmacists and nurses to unintended interchanges of drugs that can result in patient injury or death. The existing medication-use system is flawed because its safety depends on human perfection. Simplicity, standardization, differentiation, lack of duplication, and unambiguous communication are human factors concepts that are relevant to the medication-use process. These principles have often been ignored in drug naming, labeling, and packaging. Instead, current methods are based on long-standing commercial considerations and bureaucratic procedures. The process for naming a marketable drug is lengthy and complex and involves submission of a new chemical entity and patent application, generic naming, brand naming, FDA review, and final approval. Drug companies seek the fastest possible approval and may believe that the incremental benefit of human factors evaluation is small. "Trade dress" is the concept that underlies labeling and packaging issues for the drug industry. Drug companies are resistant to changing trade dress and brand names. Although a variety of private-sector organizations have called for reforms in drug naming, labeling, and packaging standards have been proposed, the problem remains. Drug names, labels, and packages are not selected and designed in accordance with human factors principles. FDA standards do not require application of these principles, the drug industry has struggled with change, and private-sector initiatives have had only limited success.

Scientific names of organisms: attribution, rights, and licensing

PubMed Central

2014-01-01

Background As biological disciplines extend into the ‘big data’ world, they will need a names-based infrastructure to index and interconnect distributed data. The infrastructure must have access to all names of all organisms if it is to manage all information. Those who compile lists of species hold different views as to the intellectual property rights that apply to the lists. This creates uncertainty that impedes the development of a much-needed infrastructure for sharing biological data in the digital world. Findings The laws in the United States of America and European Union are consistent with the position that scientific names of organisms and their compilation in checklists, classifications or taxonomic revisions are not subject to copyright. Compilations of names, such as classifications or checklists, are not creative in the sense of copyright law. Many content providers desire credit for their efforts. Conclusions A ‘blue list’ identifies elements of checklists, classifications and monographs to which intellectual property rights do not apply. To promote sharing, authors of taxonomic content, compilers, intermediaries, and aggregators should receive citable recognition for their contributions, with the greatest recognition being given to the originating authors. Mechanisms for achieving this are discussed. PMID:24495358

Letter-case information and the identification of brand names.

PubMed

Perea, Manuel; Jiménez, María; Talero, Fernanda; López-Cañada, Soraya

2015-02-01

A central tenet of most current models of visual-word recognition is that lexical units are activated on the basis of case-invariant abstract letter representations. Here, we examined this assumption by using a unique type of words: brand names. The rationale of the experiments is that brand names are archetypically printed either in lowercase (e.g., adidas) or uppercase (e.g., IKEA). This allows us to present the brand names in their standard or non-standard case configuration (e.g., adidas, IKEA vs. ADIDAS, ikea, respectively). We conducted two experiments with a brand-decision task ('is it a brand name?'): a single-presentation experiment and a masked priming experiment. Results in the single-presentation experiment revealed faster identification times of brand names in their standard case configuration than in their non-standard case configuration (i.e., adidas faster than ADIDAS; IKEA faster than ikea). In the masked priming experiment, we found faster identification times of brand names when they were preceded by an identity prime that matched its standard case configuration than when it did not (i.e., faster response times to adidas-adidas than to ADIDAS-adidas). Taken together, the present findings strongly suggest that letter-case information forms part of a brand name's graphemic information, thus posing some limits to current models of visual-word recognition. © 2014 The British Psychological Society.

"New drug" designations for new therapeutic entities: new active substance, new chemical entity, new biological entity, new molecular entity.

PubMed

Branch, Sarah K; Agranat, Israel

2014-11-13

This Perspective addresses ambiguities in designations of "new drugs" intended as new therapeutic entities (NTEs). Designation of an NTE as a new drug is significant, as it may confer regulatory exclusivity, an important incentive for development of novel compounds. Such designations differ between jurisdictions according to their drug laws and drug regulations. Chemical, biological, and innovative drugs are addressed in turn. The terms new chemical entity (NCE), new molecular entity (NME), new active substance (NAS), and new biological entity (NBE) as applied in worldwide jurisdictions are clarified. Differences between them are explored through case studies showing why new drugs have different periods of exclusivity in different jurisdictions or none at all. Finally, this Perspective recommends that in future, for the purpose of new drug compilations, NME is used for a new chemical drug, NBE for a new biological drug, and the combined designation NTE should refer to either an NME or an NBE.

NCBI disease corpus: a resource for disease name recognition and concept normalization.

PubMed

Doğan, Rezarta Islamaj; Leaman, Robert; Lu, Zhiyong

2014-02-01

knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/. Published by Elsevier Inc.

Eye movements during object recognition in visual agnosia.

PubMed

Charles Leek, E; Patterson, Candy; Paul, Matthew A; Rafal, Robert; Cristino, Filipe

2012-07-01

This paper reports the first ever detailed study about eye movement patterns during single object recognition in visual agnosia. Eye movements were recorded in a patient with an integrative agnosic deficit during two recognition tasks: common object naming and novel object recognition memory. The patient showed normal directional biases in saccades and fixation dwell times in both tasks and was as likely as controls to fixate within object bounding contour regardless of recognition accuracy. In contrast, following initial saccades of similar amplitude to controls, the patient showed a bias for short saccades. In object naming, but not in recognition memory, the similarity of the spatial distributions of patient and control fixations was modulated by recognition accuracy. The study provides new evidence about how eye movements can be used to elucidate the functional impairments underlying object recognition deficits. We argue that the results reflect a breakdown in normal functional processes involved in the integration of shape information across object structure during the visual perception of shape. Copyright © 2012 Elsevier Ltd. All rights reserved.

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

Auditory Confrontation Naming in Alzheimer’s Disease

PubMed Central

Brandt, Jason; Bakker, Arnold; Maroof, David Aaron

2010-01-01

Naming is a fundamental aspect of language and is virtually always assessed with visual confrontation tests. Tests of the ability to name objects by their characteristic sounds would be particularly useful in the assessment of visually impaired patients, and may be particularly sensitive in Alzheimer’s disease (AD). We developed an Auditory Naming Task, requiring the identification of the source of environmental sounds (i.e., animal calls, musical instruments, vehicles) and multiple-choice recognition of those not identified. In two separate studies, mild-to-moderate AD patients performed more poorly than cognitively normal elderly on the Auditory Naming Task. This task was also more difficult than two versions of a comparable Visual Naming Task, and correlated more highly with Mini-Mental State Exam score. Internal consistency reliability was acceptable, although ROC analysis revealed auditory naming to be slightly less successful than visual confrontation naming in discriminating AD patients from normal subjects. Nonetheless, our Auditory Naming Test may prove useful in research and clinical practice, especially with visually-impaired patients. PMID:20981630

A Statistical Model for Multilingual Entity Detection and Tracking

DTIC Science & Technology

2004-01-01

tomatic Content Extraction ( ACE ) evaluation achieved top-tier results in all three evaluation languages. 1 Introduction Detecting entities, whether named...of com- bining the detected mentions into groups of references to the same object. The work presented here is motivated by the ACE eval- uation...Entropy (MaxEnt henceforth) (Berger et al., 1996) and Robust Risk Minimization (RRM henceforth) 1For a description of the ACE program see http

Music Recognition in Frontotemporal Lobar Degeneration and Alzheimer Disease

PubMed Central

Johnson, Julene K; Chang, Chiung-Chih; Brambati, Simona M; Migliaccio, Raffaella; Gorno-Tempini, Maria Luisa; Miller, Bruce L; Janata, Petr

2013-01-01

Objective To compare music recognition in patients with frontotemporal dementia, semantic dementia, Alzheimer disease, and controls and to evaluate the relationship between music recognition and brain volume. Background Recognition of familiar music depends on several levels of processing. There are few studies about how patients with dementia recognize familiar music. Methods Subjects were administered tasks that assess pitch and melody discrimination, detection of pitch errors in familiar melodies, and naming of familiar melodies. Results There were no group differences on pitch and melody discrimination tasks. However, patients with semantic dementia had considerable difficulty naming familiar melodies and also scored the lowest when asked to identify pitch errors in the same melodies. Naming familiar melodies, but not other music tasks, was strongly related to measures of semantic memory. Voxel-based morphometry analysis of brain MRI showed that difficulty in naming songs was associated with the bilateral temporal lobes and inferior frontal gyrus, whereas difficulty in identifying pitch errors in familiar melodies correlated with primarily the right temporal lobe. Conclusions The results support a view that the anterior temporal lobes play a role in familiar melody recognition, and that musical functions are affected differentially across forms of dementia. PMID:21617528

Learning and Forgetting New Names and Objects in MCI and AD

ERIC Educational Resources Information Center

Gronholm-Nyman, Petra; Rinne, Juha O.; Laine, Matti

2010-01-01

We studied how subjects with mild cognitive impairment (MCI), early Alzheimer's disease (AD) and age-matched controls learned and maintained the names of unfamiliar objects that were trained with or without semantic support (object definitions). Naming performance, phonological cueing, incidental learning of the definitions and recognition of the…

Human-machine interaction to disambiguate entities in unstructured text and structured datasets

NASA Astrophysics Data System (ADS)

Ward, Kevin; Davenport, Jack

2017-05-01

Creating entity network graphs is a manual, time consuming process for an intelligence analyst. Beyond the traditional big data problems of information overload, individuals are often referred to by multiple names and shifting titles as they advance in their organizations over time which quickly makes simple string or phonetic alignment methods for entities insufficient. Conversely, automated methods for relationship extraction and entity disambiguation typically produce questionable results with no way for users to vet results, correct mistakes or influence the algorithm's future results. We present an entity disambiguation tool, DRADIS, which aims to bridge the gap between human-centric and machinecentric methods. DRADIS automatically extracts entities from multi-source datasets and models them as a complex set of attributes and relationships. Entities are disambiguated across the corpus using a hierarchical model executed in Spark allowing it to scale to operational sized data. Resolution results are presented to the analyst complete with sourcing information for each mention and relationship allowing analysts to quickly vet the correctness of results as well as correct mistakes. Corrected results are used by the system to refine the underlying model allowing analysts to optimize the general model to better deal with their operational data. Providing analysts with the ability to validate and correct the model to produce a system they can trust enables them to better focus their time on producing higher quality analysis products.

Considering context: reliable entity networks through contextual relationship extraction

NASA Astrophysics Data System (ADS)

David, Peter; Hawes, Timothy; Hansen, Nichole; Nolan, James J.

2016-05-01

Existing information extraction techniques can only partially address the problem of exploiting unreadable-large amounts text. When discussion of events and relationships is limited to simple, past-tense, factual descriptions of events, current NLP-based systems can identify events and relationships and extract a limited amount of additional information. But the simple subset of available information that existing tools can extract from text is only useful to a small set of users and problems. Automated systems need to find and separate information based on what is threatened or planned to occur, has occurred in the past, or could potentially occur. We address the problem of advanced event and relationship extraction with our event and relationship attribute recognition system, which labels generic, planned, recurring, and potential events. The approach is based on a combination of new machine learning methods, novel linguistic features, and crowd-sourced labeling. The attribute labeler closes the gap between structured event and relationship models and the complicated and nuanced language that people use to describe them. Our operational-quality event and relationship attribute labeler enables Warfighters and analysts to more thoroughly exploit information in unstructured text. This is made possible through 1) More precise event and relationship interpretation, 2) More detailed information about extracted events and relationships, and 3) More reliable and informative entity networks that acknowledge the different attributes of entity-entity relationships.

The roles of perceptual and conceptual information in face recognition.

PubMed

Schwartz, Linoy; Yovel, Galit

2016-11-01

The representation of familiar objects is comprised of perceptual information about their visual properties as well as the conceptual knowledge that we have about them. What is the relative contribution of perceptual and conceptual information to object recognition? Here, we examined this question by designing a face familiarization protocol during which participants were either exposed to rich perceptual information (viewing each face in different angles and illuminations) or with conceptual information (associating each face with a different name). Both conditions were compared with single-view faces presented with no labels. Recognition was tested on new images of the same identities to assess whether learning generated a view-invariant representation. Results showed better recognition of novel images of the learned identities following association of a face with a name label, but no enhancement following exposure to multiple face views. Whereas these findings may be consistent with the role of category learning in object recognition, face recognition was better for labeled faces only when faces were associated with person-related labels (name, occupation), but not with person-unrelated labels (object names or symbols). These findings suggest that association of meaningful conceptual information with an image shifts its representation from an image-based percept to a view-invariant concept. They further indicate that the role of conceptual information should be considered to account for the superior recognition that we have for familiar faces and objects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

NeuroNames: an ontology for the BrainInfo portal to neuroscience on the web.

PubMed

Bowden, Douglas M; Song, Evan; Kosheleva, Julia; Dubach, Mark F

2012-01-01

BrainInfo ( http://braininfo.org ) is a growing portal to neuroscientific information on the Web. It is indexed by NeuroNames, an ontology designed to compensate for ambiguities in neuroanatomical nomenclature. The 20-year old ontology continues to evolve toward the ideal of recognizing all names of neuroanatomical entities and accommodating all structural concepts about which neuroscientists communicate, including multiple concepts of entities for which neuroanatomists have yet to determine the best or 'true' conceptualization. To make the definitions of structural concepts unambiguous and terminologically consistent we created a 'default vocabulary' of unique structure names selected from existing terminology. We selected standard names by criteria designed to maximize practicality for use in verbal communication as well as computerized knowledge management. The ontology of NeuroNames accommodates synonyms and homonyms of the standard terms in many languages. It defines complex structures as models composed of primary structures, which are defined in unambiguous operational terms. NeuroNames currently relates more than 16,000 names in eight languages to some 2,500 neuroanatomical concepts. The ontology is maintained in a relational database with three core tables: Names, Concepts and Models. BrainInfo uses NeuroNames to index information by structure, to interpret users' queries and to clarify terminology on remote web pages. NeuroNames is a resource vocabulary of the NLM's Unified Medical Language System (UMLS, 2011) and the basis for the brain regions component of NIFSTD (NeuroLex, 2011). The current version has been downloaded to hundreds of laboratories for indexing data and linking to BrainInfo, which attracts some 400 visitors/day, downloading 2,000 pages/day.

Pathologic childhood aerophagia: a recognizable clinical entity.

PubMed

Gauderer, M W; Halpin, T C; Izant, R J

1981-06-01

Pathologic childhood aerophagia is a rarely recognized, often poorly treated entity that has remained almost undescribed in either the surgical or pediatric literature. In only 1 of 9 children the condition was recognized at presentation. The initial diagnosis of the others was Hirschsprung's disease (2), malabsorption syndrome (3), gastric outlet syndrome (1), constipation (1), and esophagitis (1). Five were hospitalized and two underwent surgical procedures. History disclosed a remarkably constant triad: previous normal stooling pattern, visible and often audible air swallowing and excessive flatus. Physical examination often demonstrated a markedly or intermittently distended and tympanitic abdomen. Abdominal musculature was thinned in children with chronic aerophagia. Roentgenographic evaluation showed massively distended loops of intestine throughout without associated air-fluid levels. There was marked compression of the diaphragm with limited excursion in some. Laboratory and malabsorption testing was normal. Treatment is limited to recognition of the problem, nasogastric decompression in severe cases and psychologic counseling when symptoms persist in the older child. The recognition of this condition may lead to a better understanding of its pathophysiology and will reduce the number of unnecessary admissions or surgical procedures.

Naming the newly found landforms on Venus

NASA Technical Reports Server (NTRS)

Batson, R. M.; Russell, J. F.

1991-01-01

The mapping of Venus is unique in the history of cartigraphy; never has so much territory been discovered and mapped in so short a period of time. Therefore, in the interest of international scientific communication, there is a unique urgency to the development of a system of names for surface features on Venus. The process began with the naming of features seen on radar images taken from Earth and continued through mapping expeditions of the U.S. and U.S.S.R. However, the Magellan Mission resolves features twenty-five times smaller than those mapped previously, and its radar data will cover an area nearly equivalent to that of the continents and the sea-floors of the Earth combined. The International Astronomical Union (IAU) was charged with the formal endorsement of names of features on the planets. Proposed names are collected, approved, and applied through the IAU Working Group for Planetary System Nomenclature (WGPSN) and its task groups, prior to IAU approval by the IAU General Assembly. Names approved by the WGPSN and its task groups, prior to final approval may be used on published maps and articles, provided that their provisional nature is stipulated. The IAU has established themes for the names to be used on each of the planets; names of historical and mythological women are used on Venus. Names of political entities and those identified with active religions are not acceptable, and a person must have been deceased for three years or more to be considered. Any interested person may propose a name for consideration by the IAU.

Anaplastic sarcoma of the kidney.

PubMed

Labanaris, Apostolos; Zugor, Vahudin; Smiszek, Robert; Nützel, Reinhold; Kühn, Reinhard

2009-02-15

Wilms tumor can appear with a wide spectrum of morphologic features and can sometimes cover or delay the recognition of other clinicopathologic entities of the kidney. We present a case of a new tumor entity of the kidney, namely the anaplastic sarcoma of the kidney, a tumor of high malignancy.

Call me Alix, not Elix: vowels are more important than consonants in own-name recognition at 5 months.

PubMed

Bouchon, Camillia; Floccia, Caroline; Fux, Thibaut; Adda-Decker, Martine; Nazzi, Thierry

2015-07-01

Consonants and vowels differ acoustically and articulatorily, but also functionally: Consonants are more relevant for lexical processing, and vowels for prosodic/syntactic processing. These functional biases could be powerful bootstrapping mechanisms for learning language, but their developmental origin remains unclear. The relative importance of consonants and vowels at the onset of lexical acquisition was assessed in French-learning 5-month-olds by testing sensitivity to minimal phonetic changes in their own name. Infants' reactions to mispronunciations revealed sensitivity to vowel but not consonant changes. Vowels were also more salient (on duration and intensity) but less distinct (on spectrally based measures) than consonants. Lastly, vowel (but not consonant) mispronunciation detection was modulated by acoustic factors, in particular spectrally based distance. These results establish that consonant changes do not affect lexical recognition at 5 months, while vowel changes do; the consonant bias observed later in development does not emerge until after 5 months through additional language exposure. © 2014 John Wiley & Sons Ltd.

New FASB standard addresses revenue recognition considerations.

PubMed

McKee, Thomas E

2015-12-01

Healthcare organizations are expected to apply the following steps in revenue recognition under the new standard issued in May 2014 by the Financial Accounting Standards Board: Identify the customer contract. Identify the performance obligations in the contract. Determine the transaction price. Allocate the transaction price to the performance obligations in the contract. Recognize revenue when--or in some circumstances, as--the entity satisfies the performance obligation.

Speech Processing and Recognition (SPaRe)

DTIC Science & Technology

2011-01-01

results in the areas of automatic speech recognition (ASR), speech processing, machine translation (MT), natural language processing ( NLP ), and...Processing ( NLP ), Information Retrieval (IR) 16. SECURITY CLASSIFICATION OF: UNCLASSIFED 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME...Figure 9, the IOC was only expected to provide document submission and search; automatic speech recognition (ASR) for English, Spanish, Arabic , and

Face-Name Associative Recognition Deficits in Subjective Cognitive Decline and Mild Cognitive Impairment.

PubMed

Polcher, Alexandra; Frommann, Ingo; Koppara, Alexander; Wolfsgruber, Steffen; Jessen, Frank; Wagner, Michael

2017-01-01

There is a need for more sensitive neuropsychological tests to detect subtle cognitive deficits emerging in the preclinical stage of Alzheimer's disease (AD). Associative memory is a cognitive function supported by the hippocampus and affected early in the process of AD. We developed a short computerized face-name associative recognition test (FNART) and tested whether it would detect memory impairment in memory clinic patients with mild cognitive impairment (MCI) and subjective cognitive decline (SCD). We recruited 61 elderly patients with either SCD (n = 32) or MCI (n = 29) and 28 healthy controls (HC) and compared performance on FNART, self-reported cognitive deterioration in different domains (ECog-39), and, in a reduced sample (n = 46), performance on the visual Paired Associates Learning of the CANTAB battery. A significant effect of group on FNART test performance in the total sample was found (p < 0.001). Planned contrasts indicated a significantly lower associative memory performance in the SCD (p = 0.001, d = 0.82) and MCI group (p < 0.001, d = 1.54), as compared to HCs, respectively. The CANTAB-PAL discriminated only between HC and MCI, possibly because of reduced statistical power. Adjusted for depression, performance on FNART was significantly related to ECog-39 Memory in SCD patients (p = 0.024) but not in MCI patients. Associative memory is substantially impaired in memory clinic patients with SCD and correlates specifically with memory complaints at this putative preclinical stage of AD. Further studies will need to examine the predictive validity of the FNART in SCD patients with regard to longitudinal (i.e., conversion to MCI/AD) and biomarker outcomes.

Spatial distribution and influence factors of interprovincial terrestrial physical geographical names in China

NASA Astrophysics Data System (ADS)

Zhang, S.; Wang, Y.; Ju, H.

2017-12-01

The interprovincial terrestrial physical geographical entities are the key areas of regional integrated management. Based on toponomy dictionaries and different thematic maps, the attributes and the spatial extent of the interprovincial terrestrial physical geographical names (ITPGN, including terrain ITPGN and water ITPGN) were extracted. The coefficient of variation and Moran's I were combined together to measure the spatial variation and spatial association of ITPGN. The influencing factors of the distribution of ITPGN and the implications for the regional management were further discussed. The results showed that 11325 ITPGN were extracted, including 7082 terrain ITPGN and 4243 water ITPGN. Hunan Province had the largest number of ITPGN in China, and Shanghai had the smallest number. The spatial variance of the terrain ITPGN was larger than that of the water ITPGN, and the ITPGN showed a significant agglomeration phenomenon in the southern part of China. Further analysis showed that the number of ITPGN was positively related with the relative elevation and the population where the relative elevation was lower than 2000m and the population was less than 50 million. But the number of ITPGN showed a negative relationship with the two factors when their values became larger, indicating a large number of unnamed entities existed in complex terrain areas and a decreasing number of terrestrial physical geographical entities in densely populated area. Based on these analysis, we suggest the government take the ITPGN as management units to realize a balance development between different parts of the entities and strengthen the geographical names census and the nomination of unnamed interprovincial physical geographical entities. This study also demonstrated that the methods of literature survey, coefficient of variation and Moran's I can be combined to enhance the understanding of the spatial pattern of ITPGN.

Improving recognition of hepatic perivascular epithelioid cell tumor: Case report and literature review.

PubMed

Maebayashi, Toshiya; Abe, Katsumi; Aizawa, Takuya; Sakaguchi, Masakuni; Ishibashi, Naoya; Abe, Osamu; Takayama, Tadatoshi; Nakayama, Hisashi; Matsuoka, Shunichi; Nirei, Kazushige; Nakamura, Hitomi; Ogawa, Masahiro; Sugitani, Masahiko

2015-05-07

A 58-year-old man presented with the chief complaint of abdominal bloating and was incidentally found to have a liver tumor. As diagnostic imaging studies could not rule out malignancy, the patient underwent partial resection of segment 3 of the liver. The lesion pathologically showed eosinophilic proliferation, in addition to immunohistochemical positivity for human melanoma black 45 and Melan-A, thereby leading to the diagnosis of a hepatic perivascular epithelioid cell tumor (PEComa). A PEComa arising from the liver is relatively rare. Moreover, the name 'PEComa' has not yet been widely recognized, and the same disease entity has been called epithelioid angiomyolipoma (EAML), further diminishing the recognition of PEComa. In addition, PEComa imaging findings mimic those of malignant liver tumors, and clinically, this tumor tends to enlarge. Therefore, a PEComa is difficult to diagnose. We conducted a systematic review of PEComa and EAML cases and discuss the results, including findings useful for differentiating perivascular epithelioid cell tumors from malignant liver tumors.

Improving recognition of hepatic perivascular epithelioid cell tumor: Case report and literature review

PubMed Central

Maebayashi, Toshiya; Abe, Katsumi; Aizawa, Takuya; Sakaguchi, Masakuni; Ishibashi, Naoya; Abe, Osamu; Takayama, Tadatoshi; Nakayama, Hisashi; Matsuoka, Shunichi; Nirei, Kazushige; Nakamura, Hitomi; Ogawa, Masahiro; Sugitani, Masahiko

2015-01-01

A 58-year-old man presented with the chief complaint of abdominal bloating and was incidentally found to have a liver tumor. As diagnostic imaging studies could not rule out malignancy, the patient underwent partial resection of segment 3 of the liver. The lesion pathologically showed eosinophilic proliferation, in addition to immunohistochemical positivity for human melanoma black 45 and Melan-A, thereby leading to the diagnosis of a hepatic perivascular epithelioid cell tumor (PEComa). A PEComa arising from the liver is relatively rare. Moreover, the name ‘PEComa’ has not yet been widely recognized, and the same disease entity has been called epithelioid angiomyolipoma (EAML), further diminishing the recognition of PEComa. In addition, PEComa imaging findings mimic those of malignant liver tumors, and clinically, this tumor tends to enlarge. Therefore, a PEComa is difficult to diagnose. We conducted a systematic review of PEComa and EAML cases and discuss the results, including findings useful for differentiating perivascular epithelioid cell tumors from malignant liver tumors. PMID:25954119

78 FR 78514 - Designation of One Individual and Three Entities Pursuant to Executive Order

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-26

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Designation of One Individual and... publishing the name of one individual and three entities whose property and interests in property are blocked..., Security, or Stability of Burma.'' DATES: The designation by the Director of OFAC of the one individual and...

Age Invariance in Semantic and Episodic Metamemory: Both Younger and Older Adults Provide Accurate Feeling of Knowing For Names of Faces

PubMed Central

Eakin, Deborah K.; Hertzog, Christopher; Harris, William

2013-01-01

Age differences in feeling-of-knowing (FOK) accuracy were examined for both episodic memory and semantic memory. Younger and older adults either viewed pictures of famous faces (semantic memory) or associated nonfamous faces and names (episodic memory) and were tested on their memory for the name of the presented face. Participants viewed the faces again and made a FOK prediction about future recognition of the name associated with the presented face. Finally, four-alternative forced-choice recognition memory for the name, cued by the face, was tested and confidence judgments (CJs) were collected for each recognition response. Age differences were not obtained in semantic memory or the resolution of semantic FOKs, defined by within-person correlations of FOKs with recognition memory performance. Although age differences were obtained in level of episodic memory, there were no age differences in the resolution of episodic FOKs. FOKs for correctly recognized items correlated reliably with CJs for both types of materials, and did not differ by age group. The results indicate age invariance in monitoring of retrieval processes for name-face associations. PMID:23537379

Age invariance in semantic and episodic metamemory: both younger and older adults provide accurate feeling-of-knowing for names of faces.

PubMed

Eakin, Deborah K; Hertzog, Christopher; Harris, William

2014-01-01

Age differences in feeling-of-knowing (FOK) accuracy were examined for both episodic memory and semantic memory. Younger and older adults either viewed pictures of famous faces (semantic memory) or associated non-famous faces and names (episodic memory) and were tested on their memory for the name of the presented face. Participants viewed the faces again and made a FOK prediction about future recognition of the name associated with the presented face. Finally, four-alternative forced-choice recognition memory for the name, cued by the face, was tested and confidence judgments (CJs) were collected for each recognition response. Age differences were not obtained in semantic memory or the resolution of semantic FOKs, defined by within-person correlations of FOKs with recognition memory performance. Although age differences were obtained in level of episodic memory, there were no age differences in the resolution of episodic FOKs. FOKs for correctly recognized items correlated reliably with CJs for both types of materials, and did not differ by age group. The results indicate age invariance in monitoring of retrieval processes for name-face associations.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

PubMed

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A

2017-03-01

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

PubMed Central

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A.

2017-01-01

Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. PMID:28328252

Relatedness-based Multi-Entity Summarization

PubMed Central

Gunaratna, Kalpa; Yazdavar, Amir Hossein; Thirunarayan, Krishnaprasad; Sheth, Amit; Cheng, Gong

2017-01-01

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches. PMID:29051696

13 CFR 130.200 - Eligible entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 13 Business Credit and Assistance 1 2011-01-01 2011-01-01 false Eligible entities. 130.200 Section... CENTERS § 130.200 Eligible entities. (a) Recipient Organization. The following entities are eligible to... community or junior college; (5) An entity formed by two or more of the above entities; or (6) Any entity...

31 CFR 575.304 - Entity of the Government of Iraq; Iraqi Government entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity of the Government of Iraq; Iraqi Government entity. 575.304 Section 575.304 Money and Finance: Treasury Regulations Relating to... SANCTIONS REGULATIONS General Definitions § 575.304 Entity of the Government of Iraq; Iraqi Government...

Influences on Facial Emotion Recognition in Deaf Children

ERIC Educational Resources Information Center

Sidera, Francesc; Amadó, Anna; Martínez, Laura

2017-01-01

This exploratory research is aimed at studying facial emotion recognition abilities in deaf children and how they relate to linguistic skills and the characteristics of deafness. A total of 166 participants (75 deaf) aged 3-8 years were administered the following tasks: facial emotion recognition, naming vocabulary and cognitive ability. The…

31 CFR 800.211 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 800.211 Section 800.211 Money... FOREIGN PERSONS Definitions § 800.211 Entity. The term entity means any branch, partnership, group or sub... separate legal entity) operated by any one of the foregoing as a business undertaking in a particular...

Exploring the Repeated Name Penalty and the Overt Pronoun Penalty in Spanish

ERIC Educational Resources Information Center

Gelormini-Lezama, Carlos

2018-01-01

Anaphoric expressions such as repeated names, overt pronouns, and null pronouns serve a major role in the creation and maintenance of discourse coherence. The felicitous use of an anaphoric expression is highly dependent on the discourse salience of the entity introduced by the antecedent. Gordon et al. ("Cogn Sci" 17:311-347, 1993)…

Mispronunciation Detection for Language Learning and Speech Recognition Adaptation

ERIC Educational Resources Information Center

Ge, Zhenhao

2013-01-01

The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.…

Color Makes a Difference: Two-Dimensional Object Naming in Literate and Illiterate Subjects

ERIC Educational Resources Information Center

Reis, Alexandra; Faisca, Luis; Ingvar, Martin; Petersson, Karl Magnus

2006-01-01

Previous work has shown that illiterate subjects are better at naming two-dimensional representations of real objects when presented as colored photos as compared to black and white drawings. This raises the question if color or textural details selectively improve object recognition and naming in illiterate compared to literate subjects. In this…

78 FR 37664 - Identification of Entities Pursuant to the Iranian Transactions and Sanctions Regulations and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-21

... Control (``OFAC'') is publishing the names of 38 entities identified as the Government of Iran under the... Government of Iran and Iranian Financial Institutions'' (the ``Order''). Section 1(a) of the Order blocks, with certain exceptions, all property and interests in property of the Government of Iran, including...

A database of natural products and chemical entities from marine habitat

PubMed Central

Babu, Padavala Ajay; Puppala, Suma Sree; Aswini, Satyavarapu Lakshmi; Vani, Metta Ramya; Kumar, Chinta Narasimha; Prasanna, Tallapragada

2008-01-01

Marine compound database consists of marine natural products and chemical entities, collected from various literature sources, which are known to possess bioactivity against human diseases. The database is constructed using html code. The 12 categories of 182 compounds are provided with the source, compound name, 2-dimensional structure, bioactivity and clinical trial information. The database is freely available online and can be accessed at http://www.progenebio.in/mcdb/index.htm PMID:19238254

A Capital case for common names of species of fishes--a white crappie or a White Crappie

Treesearch

Joseph S. Nelson; Wayne C. Stames; Melvin L. Warren

2002-01-01

Common names of fishes are an important and often primary means of fish biologists communicating with each other and with the public. Although common names will never replace scientific names, they are indispensable in many areas such as fisheries science, management, administration, and education. In recognition of the important role common names play in communicating...

A normal' category-specific advantage for naming living things.

PubMed

Laws, K R; Neve, C

1999-10-01

'Artefactual' accounts of category-specific disorders for living things have highlighted that compared to nonliving things, living things have lower name frequency, lower concept familiarity and greater visual complexity and greater within-category structural similarity or 'visual crowding' [7]. These hypotheses imply that deficits for living things are an exaggeration of some 'normal tendency'. Contrary to these notions, we found that normal subjects were consistently worse at naming nonliving than living things in a speeded presentation paradigm. Moreover, their naming was not predicted by concept familiarity, name frequency or visual complexity; however, a novel measure of visual familiarity (i.e. for the appearance of things) did significantly predict naming. We propose that under speeded conditions, normal subjects find nonliving things harder to name because their representations are less visually predictable than for living things (i.e. nonliving things show greater within-item structural variability). Finally, because nonliving things have multiple representations in the real world, this may lower the probability of finding impaired naming and recognition in this category.

The "Decorative" Female Model: Sexual Stimuli and the Recognition of Advertisements

ERIC Educational Resources Information Center

LaChance, Charles C.; And Others

1977-01-01

Examines the impact of the decorative or functionless female models in print advertising and indicates that models facilitate recognition of model/related information but do little to increase the recognition of brand names.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

PubMed Central

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13. Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract PMID:27504009

22 CFR 96.5 - Requirement that accrediting entity be a nonprofit or public entity.

Code of Federal Regulations, 2010 CFR

2010-04-01

... administering standards for entities providing child welfare services; or (b) A public entity (other than a... political subdivision, agency, or instrumentality thereof, that is responsible for licensing adoption agencies in a State and that has expertise in developing and administering standards for entities providing...

Lexical Competition in Non-Native Spoken-Word Recognition

ERIC Educational Resources Information Center

Weber, Andrea; Cutler, Anne

2004-01-01

Four eye-tracking experiments examined lexical competition in non-native spoken-word recognition. Dutch listeners hearing English fixated longer on distractor pictures with names containing vowels that Dutch listeners are likely to confuse with vowels in a target picture name ("pencil," given target "panda") than on less confusable distractors…

Competition in prescription drug markets: the roles of trademarks, advertising, and generic names.

PubMed

Feldman, Roger; Lobo, Félix

2013-08-01

We take on two subjects of controversy among economists-advertising and trademarks-in the context of the market for generic drugs. We outline a model in which trademarks for drug names reduce search costs but increase product differentiation. In this particular framework, trademarks may not benefit consumers. In contrast, the generic names of drugs or "International Nonproprietary Names" (INN) have unquestionable benefits in both economic theory and empirical studies. We offer a second model where advertising of a brand-name drug creates recognition for the generic name. The monopoly patent-holder advertises less than in the absence of a competitive spillover.

Song Recognition without Identification: When People Cannot "Name that Tune" but Can Recognize It as Familiar

ERIC Educational Resources Information Center

Kostic, Bogdan; Cleary, Anne M.

2009-01-01

Recognition without identification (RWI) is a common day-to-day experience (as when recognizing a face or a tune as familiar without being able to identify the person or the song). It is also a well-established laboratory-based empirical phenomenon: When identification of recognition test items is prevented, participants can discriminate between…

Learning the names of people: the role of image mediators.

PubMed

Groninger, L D; Groninger, D H; Stiens, J

1995-06-01

Four experiments are reported involving the effects of bizarre and common imagery mediation techniques on the learning and 1-week retention of surnames, given videotaped faces as cues. The videotapes contained 24 undergraduates who were photographed from about the chest up, and who introduced themselves at a 20-second rate. Experiment 1 showed that for both concrete and abstract names, immediate recall of the list was better under imagery mediation instructions than under control instructions. Experiment 2 studied the same conditions using immediate recognition memory of the list as a retrieval measure for the names, and found, despite ceiling effects, that bizarre imagery instructions facilitated recognition for concrete names. Experiment 3 showed that immediate recall could be improved if subjects were given an image mediator for every face-name pair as opposed to generating their own image mediators. Experiment 4 yielded three important findings: (a) 84% of the variance in the 1-week retention of initially recalled names was explained by the presence of absence of the original mediator during 1-week recall; (b) instructions to form image mediators facilitate recall not because image mediators are more effective than other types of mediators, but because they increase the likelihood that a mediator will be formed; (c) 1-week retention could be enhanced with an increased focus during encoding on the points where the mediation process is most likely to fail. The results of these studies are discussed within the context of mediation model wherein recall can fail at any of four stages.

Evaluating Stream Filtering for Entity Profile Updates in TREC 2012, 2013, and 2014 (KBA Track Overview, Notebook Paper)

DTIC Science & Technology

2014-11-01

possible future directions that build on the KBA experience.   Data Assets   In addition to the three hundred run submissions from diverse systems...form name of an entity and assigning a confidence score based on the number of matches of tokens in the name. See code in github [6]. macro-P...131 64 GENDER 4 2 FoundedBy     56 30 NAME 2 2 DateOfDeath     54 12 TOP_MEMBERS_EMPLOYEES 2 1 EmployeeOf     44 19 WON_AWARD 1 1

Obligatory and facultative brain regions for voice-identity recognition

PubMed Central

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Abstract Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal

78 FR 38537 - Federal Acquisition Regulation; Federal Acquisition Circular 2005-68; Small Entity Compliance Guide

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-26

... Entity Compliance Guide. SUMMARY: This document is issued under the joint authority of DOD, GSA, and NASA... whose name appears in the table below. Please cite FAC 2005-68 and the FAR case number. For information... Listed in FAC 2005-68 Subject FAR Case Analyst *Expansion of Applicability of the Senior Executive...

31 CFR 575.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 575.303 Section 575.303 Money... CONTROL, DEPARTMENT OF THE TREASURY IRAQI SANCTIONS REGULATIONS General Definitions § 575.303 Entity. The term entity includes a corporation, partnership, association, or other organization. ...

Acquired bilateral telangiectatic macules: a distinct clinical entity.

PubMed

Park, Ji-Hye; Lee, Dong Jun; Lee, Yoo-Jung; Jang, Yong Hyun; Kang, Hee Young; Kim, You Chan

2014-09-01

We evaluated 13 distinct patients with multiple telangiectatic pigmented macules confined mostly to the upper arms to determine if the clinical and histopathological features of these cases might represent a specific clinical entity. We retrospectively investigated the clinical, histopathologic, and immunohistochemical features of 13 patients with multiple telangiectatic pigmented macules on the upper arms who presented between January 2003 and December 2012. Epidermal pigmentation, melanogenic activity, melanocyte number, vascularity, epidermal thickness, and perivascular mast cell number of the specimens were evaluated. Clinically, the condition favored middle-aged men. On histopathologic examination, the lesional skin showed capillary proliferation and telangiectasia in the upper dermis. Histochemical and immunohistochemical analysis revealed basal hyperpigmentation and increased melanogenic activity in the lesional skin (P < .05). No significant difference in epidermal thickness or mast cell number was observed between the normal perilesional skin and the lesional skin. The clinical and histopathologic features of these lesions were relatively consistent in all patients. In addition, the features are quite distinct from other diseases. Based on clinical and histologic features, we suggest the name acquired bilateral telangiectatic macules for this new entity.

7 CFR 1738.16 - Eligible entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Eligible entities. 1738.16 Section 1738.16... Eligible entities. (a) RUS makes broadband loans to legally organized entities providing, or proposing to provide, broadband services in eligible rural communities. (1) Types of eligible entities include...

7 CFR 1738.16 - Eligible entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Eligible entities. 1738.16 Section 1738.16... Eligible entities. (a) RUS makes broadband loans to legally organized entities providing, or proposing to provide, broadband services in eligible rural communities. (1) Types of eligible entities include...

7 CFR 63.4 - Eligible entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 3 2011-01-01 2011-01-01 false Eligible entity. 63.4 Section 63.4 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Standards... IMPROVEMENT CENTER General Provisions Definitions § 63.4 Eligible entity. Eligible entity means an entity that...

31 CFR 551.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 551.303 Section 551.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SOMALIA SANCTIONS REGULATIONS General Definitions § 551.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 537.304 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 537.304 Section 537.304 Money... CONTROL, DEPARTMENT OF THE TREASURY BURMESE SANCTIONS REGULATIONS General Definitions § 537.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 548.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 548.303 Section 548.303 Money... CONTROL, DEPARTMENT OF THE TREASURY BELARUS SANCTIONS REGULATIONS General Definitions § 548.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 551.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 551.303 Section 551.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SOMALIA SANCTIONS REGULATIONS General Definitions § 551.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 538.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 538.303 Section 538.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SUDANESE SANCTIONS REGULATIONS General Definitions § 538.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 538.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 538.303 Section 538.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SUDANESE SANCTIONS REGULATIONS General Definitions § 538.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 536.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 536.303 Section 536.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, corporation, or other organization...

31 CFR 595.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 595.303 Section 595.303 Money... CONTROL, DEPARTMENT OF THE TREASURY TERRORISM SANCTIONS REGULATIONS General Definitions § 595.303 Entity. The term entity means a partnership, association, corporation, or other organization, group or...

31 CFR 545.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 545.303 Section 545.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, corporation, or other organization...

31 CFR 543.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 543.304 Section 543.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 541.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 541.303 Section 541.303 Money... CONTROL, DEPARTMENT OF THE TREASURY ZIMBABWE SANCTIONS REGULATIONS General Definitions § 541.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 537.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 537.304 Section 537.304 Money... CONTROL, DEPARTMENT OF THE TREASURY BURMESE SANCTIONS REGULATIONS General Definitions § 537.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 536.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 536.303 Section 536.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, corporation, or other organization...

31 CFR 595.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 595.303 Section 595.303 Money... CONTROL, DEPARTMENT OF THE TREASURY TERRORISM SANCTIONS REGULATIONS General Definitions § 595.303 Entity. The term entity means a partnership, association, corporation, or other organization, group or...

31 CFR 543.304 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 543.304 Section 543.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 549.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 549.303 Section 549.303 Money... CONTROL, DEPARTMENT OF THE TREASURY LEBANON SANCTIONS REGULATIONS General Definitions § 549.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 548.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 548.303 Section 548.303 Money... CONTROL, DEPARTMENT OF THE TREASURY BELARUS SANCTIONS REGULATIONS General Definitions § 548.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 541.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 541.303 Section 541.303 Money... CONTROL, DEPARTMENT OF THE TREASURY ZIMBABWE SANCTIONS REGULATIONS General Definitions § 541.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

47 CFR 90.1103 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Designated entities. 90.1103 Section 90.1103... Designated entities. (a) This section addresses certain issues concerning designated entities in the Location... provisions. (1) A small business is an entity that, together with its affiliates and controlling interests...

47 CFR 90.1103 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 90.1103 Section 90.1103... Designated entities. (a) This section addresses certain issues concerning designated entities in the Location... provisions. (1) A small business is an entity that, together with its affiliates and controlling interests...

31 CFR 596.308 - Person; entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Person; entity. 596.308 Section 596... General Definitions § 596.308 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a partnership, association, corporation, or other organization. ...

31 CFR 596.308 - Person; entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Person; entity. 596.308 Section 596... General Definitions § 596.308 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a partnership, association, corporation, or other organization. ...

78 FR 80381 - Federal Acquisition Regulation; Federal Acquisition Circular 2005-72; Small Entity Compliance Guide

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-31

...: Small Entity Compliance Guide. SUMMARY: This document is issued under the joint authority of DOD, GSA..., contact the analyst whose name appears in the table below. Please cite FAC 2005-72 and the FAR case number... 202- 501-4755. Rules Listed in FAC 2005-72 Item Subject FAR Case Analyst *I Service 2010-010 Loeb...

31 CFR 544.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 544.303 Section 544.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 544.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 594.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 594.303 Section 594.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, corporation, or other organization, group, or...

31 CFR 594.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 594.303 Section 594.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, corporation, or other organization, group, or...

31 CFR 598.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 598.303 Section 598.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... § 598.303 Entity. The term entity means a partnership, joint venture, association, corporation...

31 CFR 542.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 542.303 Section 542.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SYRIAN SANCTIONS REGULATIONS General Definitions § 542.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 592.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 592.303 Section 592.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 588.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 588.303 Section 588.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 561.316 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 561.316 Section 561.316 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 585.310 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 585.310 Section 585.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 585.310 Entity. The term entity includes a corporation, partnership, association, or other...

31 CFR 588.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 588.303 Section 588.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 587.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 587.303 Section 587.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... SANCTIONS REGULATIONS General Definitions § 587.303 Entity. The term entity means a partnership, association...

31 CFR 542.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 542.303 Section 542.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SYRIAN SANCTIONS REGULATIONS General Definitions § 542.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 562.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 562.303 Section 562.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... § 562.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation...

31 CFR 593.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 593.303 Section 593.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 593.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 592.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 592.303 Section 592.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 586.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 586.303 Section 586.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... REGULATIONS General Definitions § 586.303 Entity. The term entity means a partnership, association, trust...

31 CFR 546.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 546.304 Section 546.304 Money... CONTROL, DEPARTMENT OF THE TREASURY DARFUR SANCTIONS REGULATIONS General Definitions § 546.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 597.306 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 597.306 Section 597.306 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 597.306 Entity. The term entity includes a partnership, association, corporation, or other...

31 CFR 539.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 539.303 Section 539.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 539.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 544.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 544.303 Section 544.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 544.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 570.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 570.303 Section 570.303 Money... CONTROL, DEPARTMENT OF THE TREASURY LIBYAN SANCTIONS REGULATIONS General Definitions § 570.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 540.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 540.303 Section 540.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 540.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 597.306 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 597.306 Section 597.306 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 597.306 Entity. The term entity includes a partnership, association, corporation, or other...

31 CFR 540.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 540.303 Section 540.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 540.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 539.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 539.303 Section 539.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 539.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 593.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 593.303 Section 593.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 593.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 576.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 576.304 Section 576.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 576.304 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 598.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 598.303 Section 598.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... § 598.303 Entity. The term entity means a partnership, joint venture, association, corporation...

43 CFR 426.10 - Public entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 1 2011-10-01 2011-10-01 false Public entities. 426.10 Section 426.10... INTERIOR ACREAGE LIMITATION RULES AND REGULATIONS § 426.10 Public entities. (a) Application of the acreage limitation provisions to public entities. Reclamation does not subject public entities to the acreage...

43 CFR 426.10 - Public entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Public entities. 426.10 Section 426.10... INTERIOR ACREAGE LIMITATION RULES AND REGULATIONS § 426.10 Public entities. (a) Application of the acreage limitation provisions to public entities. Reclamation does not subject public entities to the acreage...

Neighborhood Frequency Effect in Chinese Word Recognition: Evidence from Naming and Lexical Decision

ERIC Educational Resources Information Center

Li, Meng-Feng; Gao, Xin-Yu; Chou, Tai-Li; Wu, Jei-Tun

2017-01-01

Neighborhood frequency is a crucial variable to know the nature of word recognition. Different from alphabetic scripts, neighborhood frequency in Chinese is usually confounded by component character frequency and neighborhood size. Three experiments were designed to explore the role of the neighborhood frequency effect in Chinese and the stimuli…

Brand name logo recognition of fast food and healthy food among children.

PubMed

Arredondo, Elva; Castaneda, Diego; Elder, John P; Slymen, Donald; Dozier, David

2009-02-01

The fast food industry has been increasingly criticized for creating brand loyalty in young consumers. Food marketers are well versed in reaching children and youth given the importance of brand loyalty on future food purchasing behavior. In addition, food marketers are increasingly targeting the Hispanic population given their growing spending power. The fast food industry is among the leaders in reaching youth and ethnic minorities through their marketing efforts. The primary objective of this study was to determine if young children recognized fast food restaurant logos at a higher rate than other food brands. Methods Children (n = 155; 53% male; 87% Hispanic) ages 4-8 years were recruited from elementary schools and asked to match 10 logo cards to products depicted on a game board. Parents completed a survey assessing demographic and psychosocial characteristics associated with a healthy lifestyle in the home. Results Older children and children who were overweight were significantly more likely to recognize fast food restaurant logos than other food logos. Moreover, parents' psychosocial and socio-demographic characteristics were associated with the type of food logo recognized by the children. Conclusions Children's high recognition of fast food restaurant logos may reflect greater exposure to fast food advertisements. Families' socio-demographic characteristics play a role in children's recognition of food logos.

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 101.1429 Section 101.1429... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with...

Layered recognition networks that pre-process, classify, and describe

NASA Technical Reports Server (NTRS)

Uhr, L.

1971-01-01

A brief overview is presented of six types of pattern recognition programs that: (1) preprocess, then characterize; (2) preprocess and characterize together; (3) preprocess and characterize into a recognition cone; (4) describe as well as name; (5) compose interrelated descriptions; and (6) converse. A computer program (of types 3 through 6) is presented that transforms and characterizes the input scene through the successive layers of a recognition cone, and then engages in a stylized conversation to describe the scene.

31 CFR 560.305 - Person; entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Person; entity. 560.305 Section 560.305 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 560.305 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a...

18 CFR 46.5 - Covered entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 18 Conservation of Power and Water Resources 1 2011-04-01 2011-04-01 false Covered entities. 46.5... FOR PERSONS HOLDING INTERLOCKING POSITIONS § 46.5 Covered entities. Entities to which the general rule..., or a savings and loan association; (b) Any entity which is authorized by law to underwrite or...

46 CFR 67.41 - Governmental entity.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 2 2010-10-01 2010-10-01 false Governmental entity. 67.41 Section 67.41 Shipping COAST... DOCUMENTATION OF VESSELS Citizenship Requirements for Vessel Documentation § 67.41 Governmental entity. A governmental entity is a citizen for the purpose of obtaining a vessel document if it is an entity of the...

46 CFR 67.41 - Governmental entity.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 2 2011-10-01 2011-10-01 false Governmental entity. 67.41 Section 67.41 Shipping COAST... DOCUMENTATION OF VESSELS Citizenship Requirements for Vessel Documentation § 67.41 Governmental entity. A governmental entity is a citizen for the purpose of obtaining a vessel document if it is an entity of the...

18 CFR 46.5 - Covered entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Covered entities. 46.5... FOR PERSONS HOLDING INTERLOCKING POSITIONS § 46.5 Covered entities. Entities to which the general rule..., or a savings and loan association; (b) Any entity which is authorized by law to underwrite or...

42 CFR 6.3 - Eligible entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 1 2010-10-01 2010-10-01 false Eligible entities. 6.3 Section 6.3 Public Health... COVERAGE OF CERTAIN GRANTEES AND INDIVIDUALS § 6.3 Eligible entities. (a) Grantees. Entities eligible for coverage under this part are public and nonprofit private entities receiving Federal funds under any of the...

31 CFR 560.305 - Person; entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Person; entity. 560.305 Section 560.305 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 560.305 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a...

42 CFR 6.3 - Eligible entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 1 2011-10-01 2011-10-01 false Eligible entities. 6.3 Section 6.3 Public Health... COVERAGE OF CERTAIN GRANTEES AND INDIVIDUALS § 6.3 Eligible entities. (a) Grantees. Entities eligible for coverage under this part are public and nonprofit private entities receiving Federal funds under any of the...

Transfer between Pose and Illumination Training in Face Recognition

ERIC Educational Resources Information Center

Liu, Chang Hong; Bhuiyan, Md. Al-Amin; Ward, James; Sui, Jie

2009-01-01

The relationship between pose and illumination learning in face recognition was examined in a yes-no recognition paradigm. The authors assessed whether pose training can transfer to a new illumination or vice versa. Results show that an extensive level of pose training through a face-name association task was able to generalize to a new…

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 2 Grants and Agreements 1 2013-01-01 2013-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... COMPENSATION INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 2 Grants and Agreements 1 2014-01-01 2014-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 2 Grants and Agreements 1 2012-01-01 2012-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... COMPENSATION INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 2 Grants and Agreements 1 2011-01-01 2011-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

31 CFR 800.212 - Foreign entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Foreign entity. 800.212 Section 800... TAKEOVERS BY FOREIGN PERSONS Definitions § 800.212 Foreign entity. (a) The term foreign entity means any... majority of the equity interest in such entity is ultimately owned by U.S. nationals is not a foreign...

Semantic effects in naming perceptual identification but not in delayed naming: implications for models and tasks.

PubMed

Wurm, Lee H; Seaman, Sean R

2008-03-01

Previous research has demonstrated that the subjective danger and usefulness of words affect lexical decision times. Usually, an interaction is found: Increasing danger predicts faster reaction times (RTs) for words low on usefulness, but increasing danger predicts slower RTs for words high on usefulness. The authors show the same interaction with immediate auditory naming. The interaction disappeared with a delayed auditory naming control experiment, suggesting that it has a perceptual basis. In an attempt to separate input (signal to ear) from output (brain to muscle) processes in word recognition, the authors ran 2 auditory perceptual identification experiments. The interaction was again significant, but performance was best for words high on both danger and usefulness. This suggests that initial demonstrations of the interaction were reflecting an output approach/withdraw response conflict induced by stimuli that are both dangerous and useful. The interaction cannot be characterized as a tradeoff of speed versus accuracy.

Using Serial and Discrete Digit Naming to Unravel Word Reading Processes

PubMed Central

Altani, Angeliki; Protopapas, Athanassios; Georgiou, George K.

2018-01-01

During reading acquisition, word recognition is assumed to undergo a developmental shift from slow serial/sublexical processing of letter strings to fast parallel processing of whole word forms. This shift has been proposed to be detected by examining the size of the relationship between serial- and discrete-trial versions of word reading and rapid naming tasks. Specifically, a strong association between serial naming of symbols and single word reading suggests that words are processed serially, whereas a strong association between discrete naming of symbols and single word reading suggests that words are processed in parallel as wholes. In this study, 429 Grade 1, 3, and 5 English-speaking Canadian children were tested on serial and discrete digit naming and word reading. Across grades, single word reading was more strongly associated with discrete naming than with serial naming of digits, indicating that short high-frequency words are processed as whole units early in the development of reading ability in English. In contrast, serial naming was not a unique predictor of single word reading across grades, suggesting that within-word sequential processing was not required for the successful recognition for this set of words. Factor mixture analysis revealed that our participants could be clustered into two classes, namely beginning and more advanced readers. Serial naming uniquely predicted single word reading only among the first class of readers, indicating that novice readers rely on a serial strategy to decode words. Yet, a considerable proportion of Grade 1 students were assigned to the second class, evidently being able to process short high-frequency words as unitized symbols. We consider these findings together with those from previous studies to challenge the hypothesis of a binary distinction between serial/sublexical and parallel/lexical processing in word reading. We argue instead that sequential processing in word reading operates on a continuum

Using Serial and Discrete Digit Naming to Unravel Word Reading Processes.

PubMed

Altani, Angeliki; Protopapas, Athanassios; Georgiou, George K

2018-01-01

During reading acquisition, word recognition is assumed to undergo a developmental shift from slow serial/sublexical processing of letter strings to fast parallel processing of whole word forms. This shift has been proposed to be detected by examining the size of the relationship between serial- and discrete-trial versions of word reading and rapid naming tasks. Specifically, a strong association between serial naming of symbols and single word reading suggests that words are processed serially, whereas a strong association between discrete naming of symbols and single word reading suggests that words are processed in parallel as wholes. In this study, 429 Grade 1, 3, and 5 English-speaking Canadian children were tested on serial and discrete digit naming and word reading. Across grades, single word reading was more strongly associated with discrete naming than with serial naming of digits, indicating that short high-frequency words are processed as whole units early in the development of reading ability in English. In contrast, serial naming was not a unique predictor of single word reading across grades, suggesting that within-word sequential processing was not required for the successful recognition for this set of words. Factor mixture analysis revealed that our participants could be clustered into two classes, namely beginning and more advanced readers. Serial naming uniquely predicted single word reading only among the first class of readers, indicating that novice readers rely on a serial strategy to decode words. Yet, a considerable proportion of Grade 1 students were assigned to the second class, evidently being able to process short high-frequency words as unitized symbols. We consider these findings together with those from previous studies to challenge the hypothesis of a binary distinction between serial/sublexical and parallel/lexical processing in word reading. We argue instead that sequential processing in word reading operates on a continuum

2 CFR 25.320 - Entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 2 Grants and Agreements 1 2011-01-01 2011-01-01 false Entity. 25.320 Section 25.320 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... CONTRACTOR REGISTRATION Definitions § 25.320 Entity. Entity, as it is used in this part, has the meaning...

Availability of Semantic Knowledge in Familiar-Only Experiences for Names

ERIC Educational Resources Information Center

Bowles, Ben; Köhler, Stefan

2014-01-01

Situations in which the name of a person is perceived as familiar but does not trigger recall of pertinent semantic knowledge are common in daily life. In current connectionist models of person recognition, such "familiar-only" experiences reflect supra-threshold activation at person-identity nodes but subthreshold activation at nodes…

Reading component skills in dyslexia: word recognition, comprehension and processing speed.

PubMed

de Oliveira, Darlene G; da Silva, Patrícia B; Dias, Natália M; Seabra, Alessandra G; Macedo, Elizeu C

2014-01-01

The cognitive model of reading comprehension (RC) posits that RC is a result of the interaction between decoding and linguistic comprehension. Recently, the notion of decoding skill was expanded to include word recognition. In addition, some studies suggest that other skills could be integrated into this model, like processing speed, and have consistently indicated that this skill influences and is an important predictor of the main components of the model, such as vocabulary for comprehension and phonological awareness of word recognition. The following study evaluated the components of the RC model and predictive skills in children and adolescents with dyslexia. 40 children and adolescents (8-13 years) were divided in a Dyslexic Group (DG; 18 children, MA = 10.78, SD = 1.66) and control group (CG 22 children, MA = 10.59, SD = 1.86). All were students from the 2nd to 8th grade of elementary school and groups were equivalent in school grade, age, gender, and IQ. Oral and RC, word recognition, processing speed, picture naming, receptive vocabulary, and phonological awareness were assessed. There were no group differences regarding the accuracy in oral and RC, phonological awareness, naming, and vocabulary scores. DG performed worse than the CG in word recognition (general score and orthographic confusion items) and were slower in naming. Results corroborated the literature regarding word recognition and processing speed deficits in dyslexia. However, dyslexics can achieve normal scores on RC test. Data supports the importance of delimitation of different reading strategies embedded in the word recognition component. The role of processing speed in reading problems remain unclear.

Recognition and identification of famous faces in patients with unilateral temporal lobe epilepsy.

PubMed

Seidenberg, Michael; Griffith, Randall; Sabsevitz, David; Moran, Maria; Haltiner, Alan; Bell, Brian; Swanson, Sara; Hammeke, Thomas; Hermann, Bruce

2002-01-01

We examined the performance of 21 patients with unilateral temporal lobe epilepsy (TLE) and hippocampal damage (10 lefts, and 11 rights) and 10 age-matched controls on the recognition and identification (name and occupation) of well-known faces. Famous face stimuli were selected from four time periods; 1970s, 1980s, 1990-1994, and 1995-1996. Differential patterns of performance were observed for the left and right TLE group across distinct face processing components. The left TLE group showed a selective impairment in naming famous faces while they performed similar to the controls in face recognition and semantic identification (i.e. occupation). In contrast, the right TLE group was impaired across all components of face memory; face recognition, semantic identification, and face naming. Face naming impairment in the left TLE group was characterized by a temporal gradient with better naming performance for famous faces from more distant time periods. Findings are discussed in terms of the role of the temporal lobe system for the acquisition, retention, and retrieval of face semantic networks, and the differential effects of lateralized temporal lobe lesions in this process.

Verifying visual properties in sentence verification facilitates picture recognition memory.

PubMed

Pecher, Diane; Zanolie, Kiki; Zeelenberg, René

2007-01-01

According to the perceptual symbols theory (Barsalou, 1999), sensorimotor simulations underlie the representation of concepts. We investigated whether recognition memory for pictures of concepts was facilitated by earlier representation of visual properties of those concepts. During study, concept names (e.g., apple) were presented in a property verification task with a visual property (e.g., shiny) or with a nonvisual property (e.g., tart). Delayed picture recognition memory was better if the concept name had been presented with a visual property than if it had been presented with a nonvisual property. These results indicate that modality-specific simulations are used for concept representation.

[Nonspecific interstitial pneumonitis: a clinicopathologic entity, histologic pattern or unclassified group of heterogeneous interstitial pneumonitis?].

PubMed

Morais, António; Moura, M Conceição Souto; Cruz, M Rosa; Gomes, Isabel

2004-01-01

Nonspecific interstitial pneumonitis (NSIP) initially described by Katzenstein and Fiorelli in 1994, seems to be a distinct clinicopathologic entity among idiopathic interstitial pneumonitis (IIP). Besides different histologic features from other IIP, NSIP is characterized by a better long-term outcome, associated with a better steroids responsiveness than idiopathic pulmonar fibrosis (IPF), where usually were included. Thus, differentiating NSIP from other IIP, namely IPF is very significant, since it has important therapeutic and prognostic implications. NSIP encloses different pathologies, namely those with inflammatory predominance (cellular subtype) or fibrous predominance (fibrosing subtype). NSIP is reviewed and discussed by the authors, after two clinical cases description.

Influences of spoken word planning on speech recognition.

PubMed

Roelofs, Ardi; Ozdemir, Rebecca; Levelt, Willem J M

2007-09-01

In 4 chronometric experiments, influences of spoken word planning on speech recognition were examined. Participants were shown pictures while hearing a tone or a spoken word presented shortly after picture onset. When a spoken word was presented, participants indicated whether it contained a prespecified phoneme. When the tone was presented, they indicated whether the picture name contained the phoneme (Experiment 1) or they named the picture (Experiment 2). Phoneme monitoring latencies for the spoken words were shorter when the picture name contained the prespecified phoneme compared with when it did not. Priming of phoneme monitoring was also obtained when the phoneme was part of spoken nonwords (Experiment 3). However, no priming of phoneme monitoring was obtained when the pictures required no response in the experiment, regardless of monitoring latency (Experiment 4). These results provide evidence that an internal phonological pathway runs from spoken word planning to speech recognition and that active phonological encoding is a precondition for engaging the pathway. 2007 APA

Automatic vigilance for negative words in lexical decision and naming: comment on Larsen, Mercer, and Balota (2006).

PubMed

Estes, Zachary; Adelman, James S

2008-08-01

An automatic vigilance hypothesis states that humans preferentially attend to negative stimuli, and this attention to negative valence disrupts the processing of other stimulus properties. Thus, negative words typically elicit slower color naming, word naming, and lexical decisions than neutral or positive words. Larsen, Mercer, and Balota analyzed the stimuli from 32 published studies, and they found that word valence was confounded with several lexical factors known to affect word recognition. Indeed, with these lexical factors covaried out, Larsen et al. found no evidence of automatic vigilance. The authors report a more sensitive analysis of 1011 words. Results revealed a small but reliable valence effect, such that negative words (e.g., "shark") elicit slower lexical decisions and naming than positive words (e.g., "beach"). Moreover, the relation between valence and recognition was categorical rather than linear; the extremity of a word's valence did not affect its recognition. This valence effect was not attributable to word length, frequency, orthographic neighborhood size, contextual diversity, first phoneme, or arousal. Thus, the present analysis provides the most powerful demonstration of automatic vigilance to date.

Obligatory and facultative brain regions for voice-identity recognition.

PubMed

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal lobe is

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.

PubMed

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13.Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract. © The Author(s) 2016. Published by Oxford University Press.

Wolf in Sheep's Clothing: Primary Lung Cancer Mimicking Benign Entities.

PubMed

Snoeckx, Annemie; Dendooven, Amélie; Carp, Laurens; Desbuquoit, Damien; Spinhoven, Maarten J; Lauwers, Patrick; Van Schil, Paul E; van Meerbeeck, Jan P; Parizel, Paul M

2017-10-01

Lung cancer is the most common cancer worldwide. On imaging, it typically presents as mass or nodule. Recognition of these typical cases is often straightforward, whereas diagnosis of uncommon manifestations of primary lung cancer is far more challenging. Lung cancer can mimic a variety of benign entities, including pneumonia, lung abscess, postinfectious scarring, atelectasis, a mediastinal mass, emphysema and granulomatous diseases. Correlation with previous history, clinical and biochemical parameters is necessary in the assessment of these cases, but often aspecific and inconclusive. Whereas 18 F-fluorodeoxyglucose ( 18 F-FDG) Positron Emission Tomography is the cornerstone in staging of lung cancer, its role in diagnosis of these uncommon manifestations is less straightforward since benign entities can present with increased 18 F-FDG-uptake and, on the other hand, a number of these uncommon lung cancer manifestations do not exhibit increased uptake. Chest Computed Tomography (CT) is the imaging modality of choice for both lesion detection and characterization. In this pictorial review we present the wide imaging spectrum of CT-findings as well as radiologic-pathologic correlation of these uncommon lung cancer manifestations. Knowledge of the many faces of lung cancer is crucial for early diagnosis and subsequent treatment. A multidisciplinary approach in these cases is mandatory. Copyright © 2017 Elsevier B.V. All rights reserved.

Evidence for a Limited-Cascading Account of Written Word Naming

ERIC Educational Resources Information Center

Bonin, Patrick; Roux, Sebastien; Barry, Christopher; Canell, Laura

2012-01-01

We address the issue of how information flows within the written word production system by examining written object-naming latencies. We report 4 experiments in which we manipulate variables assumed to have their primary impact at the level of object recognition (e.g., quality of visual presentation of pictured objects), at the level of semantic…

Wide-threat detection: recognition of adversarial missions and activity patterns in Empire Challenge 2009

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Shabarekh, Charlotte; Furjanic, Caitlin

2011-06-01

In this paper, we present results of adversarial activity recognition using data collected in the Empire Challenge (EC 09) exercise. The EC09 experiment provided an opportunity to evaluate our probabilistic spatiotemporal mission recognition algorithms using the data from live air-born and ground sensors. Using ambiguous and noisy data about locations of entities and motion events on the ground, the algorithms inferred the types and locations of OPFOR activities, including reconnaissance, cache runs, IED emplacements, logistics, and planning meetings. In this paper, we present detailed summary of the validation study and recognition accuracy results. Our algorithms were able to detect locations and types of over 75% of hostile activities in EC09 while producing 25% false alarms.

Chemical name extraction based on automatic training data generation and rich feature set.

PubMed

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

47 CFR 80.1252 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 80.1252 Section 80.1252... MARITIME SERVICES Competitive Bidding Procedures § 80.1252 Designated entities. (a) This section addresses certain issues concerning designated entities in maritime communications services subject to competitive...

47 CFR 80.1252 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Designated entities. 80.1252 Section 80.1252... MARITIME SERVICES Competitive Bidding Procedures § 80.1252 Designated entities. (a) This section addresses certain issues concerning designated entities in maritime communications services subject to competitive...

Fast cat-eye effect target recognition based on saliency extraction

NASA Astrophysics Data System (ADS)

Li, Li; Ren, Jianlin; Wang, Xingbin

2015-09-01

Background complexity is a main reason that results in false detection in cat-eye target recognition. Human vision has selective attention property which can help search the salient target from complex unknown scenes quickly and precisely. In the paper, we propose a novel cat-eye effect target recognition method named Multi-channel Saliency Processing before Fusion (MSPF). This method combines traditional cat-eye target recognition with the selective characters of visual attention. Furthermore, parallel processing enables it to achieve fast recognition. Experimental results show that the proposed method performs better in accuracy, robustness and speed compared to other methods.

31 CFR 543.304 - Entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... CONTROL, DEPARTMENT OF THE TREASURY CôTE D'IVOIRE SANCTIONS REGULATIONS General Definitions § 543.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

47 CFR 22.229 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 22.229 Section 22.229... Licensing Requirements and Procedures Competitive Bidding Procedures § 22.229 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its...

47 CFR 24.321 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 24.321 Section 24.321... SERVICES Competitive Bidding Procedures for Narrowband PCS § 24.321 Designated entities. (a) Eligibility for small business provisions. (1) A small business is an entity that, together with its controlling...

47 CFR 22.882 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 22.882 Section 22.882...-Ground Radiotelephone Service Commercial Aviation Air-Ground Systems § 22.882 Designated entities. (a... business is an entity that, together with its affiliates, its controlling interests and the affiliates of...

47 CFR 27.1218 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.1218 Section 27.1218... COMMUNICATIONS SERVICES Broadband Radio Service and Educational Broadband Service § 27.1218 Designated entities. (a) Eligibility for small business provisions. (1) A small business is an entity that, together with...

47 CFR 24.321 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 24.321 Section 24.321... SERVICES Competitive Bidding Procedures for Narrowband PCS § 24.321 Designated entities. (a) Eligibility for small business provisions. (1) A small business is an entity that, together with its controlling...

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

PubMed

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

1998-05-01

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

47 CFR 101.538 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 101.538 Section 101.538... SERVICES 24 GHz Service and Digital Electronic Message Service § 101.538 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its...

47 CFR 27.906 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 27.906 Section 27.906... COMMUNICATIONS SERVICES 1670-1675 MHz Band § 27.906 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its controlling interests and...

46 CFR 403.110 - Accounting entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 8 2011-10-01 2011-10-01 false Accounting entities. 403.110 Section 403.110 Shipping... ACCOUNTING SYSTEM General § 403.110 Accounting entities. Each Association shall be a separate accounting entity. However, the records shall be maintained with sufficient particularity to allocate items to each...

47 CFR 27.702 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.702 Section 27.702... COMMUNICATIONS SERVICES Competitive Bidding Procedures for the 698-746 MHz Band § 27.702 Designated entities. (a) Eligibility for small business provisions. (1) An entrepreneur is an entity that, together with its...

47 CFR 27.906 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.906 Section 27.906... COMMUNICATIONS SERVICES 1670-1675 MHz Band § 27.906 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its controlling interests and...

47 CFR 22.223 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 22.223 Section 22.223... Licensing Requirements and Procedures Competitive Bidding Procedures § 22.223 Designated entities. (a) Scope... sections. (b) A small business is an entity that either: (1) Together with its affiliates and controlling...

47 CFR 22.223 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 22.223 Section 22.223... Licensing Requirements and Procedures Competitive Bidding Procedures § 22.223 Designated entities. (a) Scope... sections. (b) A small business is an entity that either: (1) Together with its affiliates and controlling...

Information Tailoring Enhancements for Large-Scale Social Data

DTIC Science & Technology

2016-06-15

Intelligent Automation Incorporated Information Tailoring Enhancements for Large-Scale... Automation Incorporated Progress Report No. 3 Information Tailoring Enhancements for Large-Scale Social Data Submitted in accordance with...1 Work Performed within This Reporting Period .................................................... 2 1.1 Enhanced Named Entity Recognition (NER

47 CFR 27.502 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 27.502 Section 27.502... COMMUNICATIONS SERVICES Competitive Bidding Procedures for the 698-806 MHz Band § 27.502 Designated entities. Eligibility for small business provisions: (a)(1) A small business is an entity that, together with its...

47 CFR 27.502 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.502 Section 27.502... COMMUNICATIONS SERVICES Competitive Bidding Procedures for the 698-806 MHz Band § 27.502 Designated entities. Eligibility for small business provisions: (a)(1) A small business is an entity that, together with its...

Evaluation Methods of The Text Entities

ERIC Educational Resources Information Center

Popa, Marius

2006-01-01

The paper highlights some evaluation methods to assess the quality characteristics of the text entities. The main concepts used in building and evaluation processes of the text entities are presented. Also, some aggregated metrics for orthogonality measurements are presented. The evaluation process for automatic evaluation of the text entities is…

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with... three years. (b) Bidding credits. A winning bidder that qualifies as a very small business, as defined...

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with... three years. (b) Bidding credits. A winning bidder that qualifies as a very small business, as defined...

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with... three years. (b) Bidding credits. A winning bidder that qualifies as a very small business, as defined...

42 CFR 438.808 - Exclusion of entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 4 2010-10-01 2010-10-01 false Exclusion of entities. 438.808 Section 438.808... Exclusion of entities. (a) General rule. FFP is available in payments under MCO contracts only if the State excludes from the contracts any entities described in paragraph (b) of this section. (b) Entities that must...

42 CFR 438.808 - Exclusion of entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 4 2011-10-01 2011-10-01 false Exclusion of entities. 438.808 Section 438.808... Exclusion of entities. (a) General rule. FFP is available in payments under MCO contracts only if the State excludes from the contracts any entities described in paragraph (b) of this section. (b) Entities that must...

16 CFR 801.50 - Formation of unincorporated entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Formation of unincorporated entities. 801.50... of unincorporated entities. (a) In the formation of an unincorporated entity (other than in... entity and the unincorporated entity itself may, in the formation transaction, be both acquiring and...

16 CFR 801.50 - Formation of unincorporated entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Formation of unincorporated entities. 801.50... of unincorporated entities. (a) In the formation of an unincorporated entity (other than in... entity and the unincorporated entity itself may, in the formation transaction, be both acquiring and...

Entitymetrics: Measuring the Impact of Entities

PubMed Central

Ding, Ying; Song, Min; Han, Jia; Yu, Qi; Yan, Erjia; Lin, Lili; Chambers, Tamy

2013-01-01

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD. PMID:24009660

47 CFR 27.807 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.807 Section 27.807... COMMUNICATIONS SERVICES 1.4 GHz Band § 27.807 Designated entities. (a) Eligibility for small business provisions...-1392 MHz band. (1) A very small business is an entity that, together with its controlling interests and...

47 CFR 27.807 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 27.807 Section 27.807... COMMUNICATIONS SERVICES 1.4 GHz Band § 27.807 Designated entities. (a) Eligibility for small business provisions...-1392 MHz band. (1) A very small business is an entity that, together with its controlling interests and...

42 CFR 425.104 - Legal entity.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 42 Public Health 3 2012-10-01 2012-10-01 false Legal entity. 425.104 Section 425.104 Public Health....104 Legal entity. (a) An ACO must be a legal entity, formed under applicable State, Federal, or Tribal... in this part. (b) An ACO formed by two or more otherwise independent ACO participants must be a legal...

42 CFR 425.104 - Legal entity.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 42 Public Health 3 2014-10-01 2014-10-01 false Legal entity. 425.104 Section 425.104 Public Health....104 Legal entity. (a) An ACO must be a legal entity, formed under applicable State, Federal, or Tribal... in this part. (b) An ACO formed by two or more otherwise independent ACO participants must be a legal...

42 CFR 425.104 - Legal entity.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 42 Public Health 3 2013-10-01 2013-10-01 false Legal entity. 425.104 Section 425.104 Public Health....104 Legal entity. (a) An ACO must be a legal entity, formed under applicable State, Federal, or Tribal... in this part. (b) An ACO formed by two or more otherwise independent ACO participants must be a legal...

CNN-based ranking for biomedical entity normalization.

PubMed

Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Huang, Dong

2017-10-03

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

Training Letter and Orthographic Pattern Recognition in Children with Slow Naming Speed

ERIC Educational Resources Information Center

Conrad, Nicole J.; Levy, Betty Ann

2011-01-01

Although research has established that performance on a rapid automatized naming (RAN) task is related to reading, the nature of this relationship is unclear. Bowers (2001) proposed that processes underlying performance on the RAN task and orthographic knowledge make independent and additive contributions to reading performance. We examined the…

Optimized Periocular Template Selection for Human Recognition

PubMed Central

Sa, Pankaj K.; Majhi, Banshidhar

2013-01-01

A novel approach for selecting a rectangular template around periocular region optimally potential for human recognition is proposed. A comparatively larger template of periocular image than the optimal one can be slightly more potent for recognition, but the larger template heavily slows down the biometric system by making feature extraction computationally intensive and increasing the database size. A smaller template, on the contrary, cannot yield desirable recognition though the smaller template performs faster due to low computation for feature extraction. These two contradictory objectives (namely, (a) to minimize the size of periocular template and (b) to maximize the recognition through the template) are aimed to be optimized through the proposed research. This paper proposes four different approaches for dynamic optimal template selection from periocular region. The proposed methods are tested on publicly available unconstrained UBIRISv2 and FERET databases and satisfactory results have been achieved. Thus obtained template can be used for recognition of individuals in an organization and can be generalized to recognize every citizen of a nation. PMID:23984370

Recognition is Used as One Cue Among Others in Judgment and Decision Making

ERIC Educational Resources Information Center

Richter, Tobias; Spath, Pamela

2006-01-01

Three experiments with paired comparisons were conducted to test the noncompensatory character of the recognition heuristic (D. G. Goldstein & G. Gigerenzer, 2002) in judgment and decision making. Recognition and knowledge about the recognized alternative were manipulated. In Experiment 1, participants were presented pairs of animal names where…

Digital signal processing algorithms for automatic voice recognition

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1987-01-01

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

Acquired prosopagnosia without word recognition deficits.

PubMed

Susilo, Tirta; Wright, Victoria; Tree, Jeremy J; Duchaine, Bradley

2015-01-01

It has long been suggested that face recognition relies on specialized mechanisms that are not involved in visual recognition of other object categories, including those that require expert, fine-grained discrimination at the exemplar level such as written words. But according to the recently proposed many-to-many theory of object recognition (MTMT), visual recognition of faces and words are carried out by common mechanisms [Behrmann, M., & Plaut, D. C. ( 2013 ). Distributed circuits, not circumscribed centers, mediate visual recognition. Trends in Cognitive Sciences, 17, 210-219]. MTMT acknowledges that face and word recognition are lateralized, but posits that the mechanisms that predominantly carry out face recognition still contribute to word recognition and vice versa. MTMT makes a key prediction, namely that acquired prosopagnosics should exhibit some measure of word recognition deficits. We tested this prediction by assessing written word recognition in five acquired prosopagnosic patients. Four patients had lesions limited to the right hemisphere while one had bilateral lesions with more pronounced lesions in the right hemisphere. The patients completed a total of seven word recognition tasks: two lexical decision tasks and five reading aloud tasks totalling more than 1200 trials. The performances of the four older patients (3 female, age range 50-64 years) were compared to those of 12 older controls (8 female, age range 56-66 years), while the performances of the younger prosopagnosic (male, 31 years) were compared to those of 14 younger controls (9 female, age range 20-33 years). We analysed all results at the single-patient level using Crawford's t-test. Across seven tasks, four prosopagnosics performed as quickly and accurately as controls. Our results demonstrate that acquired prosopagnosia can exist without word recognition deficits. These findings are inconsistent with a key prediction of MTMT. They instead support the hypothesis that face

76 FR 78146 - Addition of Certain Persons to the Entity List; and Implementation of Entity List Annual Review...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-16

... of the annual review, and revises the entry concerning one person located in Malaysia to add an... Entity List. This rule implements the results of the annual review for entities located in Malaysia... during the annual review, this rule amends one entry currently on the Entity List under Malaysia by...

Leveraging Pattern Semantics for Extracting Entities in Enterprises.

PubMed

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-05-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises ("Blue" refers to "Windows Blue"), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach.

Implicit Multisensory Associations Influence Voice Recognition

PubMed Central

von Kriegstein, Katharina; Giraud, Anne-Lise

2006-01-01

Natural objects provide partially redundant information to the brain through different sensory modalities. For example, voices and faces both give information about the speech content, age, and gender of a person. Thanks to this redundancy, multimodal recognition is fast, robust, and automatic. In unimodal perception, however, only part of the information about an object is available. Here, we addressed whether, even under conditions of unimodal sensory input, crossmodal neural circuits that have been shaped by previous associative learning become activated and underpin a performance benefit. We measured brain activity with functional magnetic resonance imaging before, while, and after participants learned to associate either sensory redundant stimuli, i.e. voices and faces, or arbitrary multimodal combinations, i.e. voices and written names, ring tones, and cell phones or brand names of these cell phones. After learning, participants were better at recognizing unimodal auditory voices that had been paired with faces than those paired with written names, and association of voices with faces resulted in an increased functional coupling between voice and face areas. No such effects were observed for ring tones that had been paired with cell phones or names. These findings demonstrate that brief exposure to ecologically valid and sensory redundant stimulus pairs, such as voices and faces, induces specific multisensory associations. Consistent with predictive coding theories, associative representations become thereafter available for unimodal perception and facilitate object recognition. These data suggest that for natural objects effective predictive signals can be generated across sensory systems and proceed by optimization of functional connectivity between specialized cortical sensory modules. PMID:17002519

Implicit multisensory associations influence voice recognition.

PubMed

von Kriegstein, Katharina; Giraud, Anne-Lise

2006-10-01

Natural objects provide partially redundant information to the brain through different sensory modalities. For example, voices and faces both give information about the speech content, age, and gender of a person. Thanks to this redundancy, multimodal recognition is fast, robust, and automatic. In unimodal perception, however, only part of the information about an object is available. Here, we addressed whether, even under conditions of unimodal sensory input, crossmodal neural circuits that have been shaped by previous associative learning become activated and underpin a performance benefit. We measured brain activity with functional magnetic resonance imaging before, while, and after participants learned to associate either sensory redundant stimuli, i.e. voices and faces, or arbitrary multimodal combinations, i.e. voices and written names, ring tones, and cell phones or brand names of these cell phones. After learning, participants were better at recognizing unimodal auditory voices that had been paired with faces than those paired with written names, and association of voices with faces resulted in an increased functional coupling between voice and face areas. No such effects were observed for ring tones that had been paired with cell phones or names. These findings demonstrate that brief exposure to ecologically valid and sensory redundant stimulus pairs, such as voices and faces, induces specific multisensory associations. Consistent with predictive coding theories, associative representations become thereafter available for unimodal perception and facilitate object recognition. These data suggest that for natural objects effective predictive signals can be generated across sensory systems and proceed by optimization of functional connectivity between specialized cortical sensory modules.

77 FR 31843 - Unnamed Entity

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-30

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. EL12-70-000] Unnamed Entity v. California Independent System Operator Corp.; Notice of Complaint Take notice that on May 21... Commission's Rules of Practice and Procedure, 18 CFR part 206, Unnamed Entity (Complainant) filed a formal...

45 CFR 160.310 - Responsibilities of covered entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Responsibilities of covered entities. 160.310... Responsibilities of covered entities. (a) Provide records and compliance reports. A covered entity must keep such... entity has complied or is complying with the applicable administrative simplification provisions. (b...

22 CFR 96.103 - Oversight by accrediting entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 22 Foreign Relations 1 2011-04-01 2011-04-01 false Oversight by accrediting entities. 96.103... Relating to Temporary Accreditation § 96.103 Oversight by accrediting entities. (a) The accrediting entity... agency's application for full accreditation when it is filed. The accrediting entity must also...

22 CFR 96.103 - Oversight by accrediting entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Oversight by accrediting entities. 96.103... Relating to Temporary Accreditation § 96.103 Oversight by accrediting entities. (a) The accrediting entity... agency's application for full accreditation when it is filed. The accrediting entity must also...

45 CFR 160.310 - Responsibilities of covered entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Responsibilities of covered entities. 160.310... Responsibilities of covered entities. (a) Provide records and compliance reports. A covered entity must keep such... entity has complied or is complying with the applicable administrative simplification provisions. (b...

How reading differs from object naming at the neuronal level.

PubMed

Price, C J; McCrory, E; Noppeney, U; Mechelli, A; Moore, C J; Biggio, N; Devlin, J T

2006-01-15

This paper uses whole brain functional neuroimaging in neurologically normal participants to explore how reading aloud differs from object naming in terms of neuronal implementation. In the first experiment, we directly compared brain activation during reading aloud and object naming. This revealed greater activation for reading in bilateral premotor, left posterior superior temporal and precuneus regions. In a second experiment, we segregated the object-naming system into object recognition and speech production areas by factorially manipulating the presence or absence of objects (pictures of objects or their meaningless scrambled counterparts) with the presence or absence of speech production (vocal vs. finger press responses). This demonstrated that the areas associated with speech production (object naming and repetitively saying "OK" to meaningless scrambled pictures) corresponded exactly to the areas where responses were higher for reading aloud than object naming in Experiment 1. Collectively the results suggest that, relative to object naming, reading increases the demands on shared speech production processes. At a cognitive level, enhanced activation for reading in speech production areas may reflect the multiple and competing phonological codes that are generated from the sublexical parts of written words. At a neuronal level, it may reflect differences in the speed with which different areas are activated and integrate with one another.

31 CFR 546.304 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 546.304 Section 546.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY DARFUR SANCTIONS REGULATIONS General Definitions § 546.304 Entity. The...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 31 Money and Finance:Treasury 3 2013-07-01 2013-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 31 Money and Finance:Treasury 3 2014-07-01 2014-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 31 Money and Finance:Treasury 3 2012-07-01 2012-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

Bilingual Language Switching: Production vs. Recognition

PubMed Central

Mosca, Michela; de Bot, Kees

2017-01-01

This study aims at assessing how bilinguals select words in the appropriate language in production and recognition while minimizing interference from the non-appropriate language. Two prominent models are considered which assume that when one language is in use, the other is suppressed. The Inhibitory Control (IC) model suggests that, in both production and recognition, the amount of inhibition on the non-target language is greater for the stronger compared to the weaker language. In contrast, the Bilingual Interactive Activation (BIA) model proposes that, in language recognition, the amount of inhibition on the weaker language is stronger than otherwise. To investigate whether bilingual language production and recognition can be accounted for by a single model of bilingual processing, we tested a group of native speakers of Dutch (L1), advanced speakers of English (L2) in a bilingual recognition and production task. Specifically, language switching costs were measured while participants performed a lexical decision (recognition) and a picture naming (production) task involving language switching. Results suggest that while in language recognition the amount of inhibition applied to the non-appropriate language increases along with its dominance as predicted by the IC model, in production the amount of inhibition applied to the non-relevant language is not related to language dominance, but rather it may be modulated by speakers' unconscious strategies to foster the weaker language. This difference indicates that bilingual language recognition and production might rely on different processing mechanisms and cannot be accounted within one of the existing models of bilingual language processing. PMID:28638361

Bilingual Language Switching: Production vs. Recognition.

PubMed

Mosca, Michela; de Bot, Kees

2017-01-01

This study aims at assessing how bilinguals select words in the appropriate language in production and recognition while minimizing interference from the non-appropriate language. Two prominent models are considered which assume that when one language is in use, the other is suppressed. The Inhibitory Control (IC) model suggests that, in both production and recognition, the amount of inhibition on the non-target language is greater for the stronger compared to the weaker language. In contrast, the Bilingual Interactive Activation (BIA) model proposes that, in language recognition, the amount of inhibition on the weaker language is stronger than otherwise. To investigate whether bilingual language production and recognition can be accounted for by a single model of bilingual processing, we tested a group of native speakers of Dutch (L1), advanced speakers of English (L2) in a bilingual recognition and production task. Specifically, language switching costs were measured while participants performed a lexical decision (recognition) and a picture naming (production) task involving language switching. Results suggest that while in language recognition the amount of inhibition applied to the non-appropriate language increases along with its dominance as predicted by the IC model, in production the amount of inhibition applied to the non-relevant language is not related to language dominance, but rather it may be modulated by speakers' unconscious strategies to foster the weaker language. This difference indicates that bilingual language recognition and production might rely on different processing mechanisms and cannot be accounted within one of the existing models of bilingual language processing.

45 CFR 162.923 - Requirements for covered entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Requirements for covered entities. 162.923 Section... Requirements for covered entities. (a) General rule. Except as otherwise provided in this part, if a covered entity conducts, with another covered entity that is required to comply with a transaction standard...

45 CFR 150.307 - Notice to responsible entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Notice to responsible entities. 150.307 Section... and Non-Federal Governmental Plans-Civil Money Penalties § 150.307 Notice to responsible entities. If... responsible entity or entities identified under § 150.305. The notice does the following: (a) Describes the...

45 CFR 162.923 - Requirements for covered entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Requirements for covered entities. 162.923 Section... Requirements for covered entities. (a) General rule. Except as otherwise provided in this part, if a covered entity conducts, with another covered entity that is required to comply with a transaction standard...

45 CFR 150.307 - Notice to responsible entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Notice to responsible entities. 150.307 Section... and Non-Federal Governmental Plans-Civil Money Penalties § 150.307 Notice to responsible entities. If... responsible entity or entities identified under § 150.305. The notice does the following: (a) Describes the...

12 CFR 1237.10 - Limited-life regulated entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 10 2014-01-01 2014-01-01 false Limited-life regulated entities. 1237.10... RECEIVERSHIP Limited-Life Regulated Entities § 1237.10 Limited-life regulated entities. (a) Status. The United... liquidity portfolio of a limited-life regulated entity. (c) Policies and procedures. The Agency may draft...

12 CFR 1237.10 - Limited-life regulated entities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 9 2013-01-01 2013-01-01 false Limited-life regulated entities. 1237.10... RECEIVERSHIP Limited-Life Regulated Entities § 1237.10 Limited-life regulated entities. (a) Status. The United... liquidity portfolio of a limited-life regulated entity. (c) Policies and procedures. The Agency may draft...

12 CFR 1237.10 - Limited-life regulated entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 9 2012-01-01 2012-01-01 false Limited-life regulated entities. 1237.10... RECEIVERSHIP Limited-Life Regulated Entities § 1237.10 Limited-life regulated entities. (a) Status. The United... liquidity portfolio of a limited-life regulated entity. (c) Policies and procedures. The Agency may draft...

Survey-based naming conventions for use in OBO Foundry ontology development

PubMed Central

Schober, Daniel; Smith, Barry; Lewis, Suzanna E; Kusnierczyk, Waclaw; Lomax, Jane; Mungall, Chris; Taylor, Chris F; Rocca-Serra, Philippe; Sansone, Susanna-Assunta

2009-01-01

Background A wide variety of ontologies relevant to the biological and medical domains are available through the OBO Foundry portal, and their number is growing rapidly. Integration of these ontologies, while requiring considerable effort, is extremely desirable. However, heterogeneities in format and style pose serious obstacles to such integration. In particular, inconsistencies in naming conventions can impair the readability and navigability of ontology class hierarchies, and hinder their alignment and integration. While other sources of diversity are tremendously complex and challenging, agreeing a set of common naming conventions is an achievable goal, particularly if those conventions are based on lessons drawn from pooled practical experience and surveys of community opinion. Results We summarize a review of existing naming conventions and highlight certain disadvantages with respect to general applicability in the biological domain. We also present the results of a survey carried out to establish which naming conventions are currently employed by OBO Foundry ontologies and to determine what their special requirements regarding the naming of entities might be. Lastly, we propose an initial set of typographic, syntactic and semantic conventions for labelling classes in OBO Foundry ontologies. Conclusion Adherence to common naming conventions is more than just a matter of aesthetics. Such conventions provide guidance to ontology creators, help developers avoid flaws and inaccuracies when editing, and especially when interlinking, ontologies. Common naming conventions will also assist consumers of ontologies to more readily understand what meanings were intended by the authors of ontologies used in annotating bodies of data. PMID:19397794

Leveraging Pattern Semantics for Extracting Entities in Enterprises

PubMed Central

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-01-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises (“Blue” refers to “Windows Blue”), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach. PMID:26705540

Determinants of naming latencies, object comprehension times, and new norms for the Russian standardized set of the colorized version of the Snodgrass and Vanderwart pictures.

PubMed

Bonin, Patrick; Guillemard-Tsaparina, Diana; Méot, Alain

2013-09-01

We report object-naming and object recognition times collected from Russian native speakers for the colorized version of the Snodgrass and Vanderwart (Journal of Experimental Psychology: Human Learning and Memory 6:174-215, 1980) pictures (Rossion & Pourtois, Perception 33:217-236, 2004). New norms for image variability, body-object interaction [BOI], and subjective frequency collected in Russian, as well as new name agreement scores for the colorized pictures in French, are also reported. In both object-naming and object comprehension times, the name agreement, image agreement, and age-of-acquisition variables made significant independent contributions. Objective word frequency was reliable in object-naming latencies only. The variables of image variability, BOI, and subjective frequency were not significant in either object naming or object comprehension. Finally, imageability was reliable in both tasks. The new norms and object-naming and object recognition times are provided as supplemental materials.

Handwritten Word Recognition Using Multi-view Analysis

NASA Astrophysics Data System (ADS)

de Oliveira, J. J.; de A. Freitas, C. O.; de Carvalho, J. M.; Sabourin, R.

This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a zoning mechanism (pseudo-segmenter), a feature extractor and a classifier is defined. The proposed application is the recognition of the Portuguese handwritten names of the months, for which a best recognition rate of 97.7% was obtained, using classifier combination.

22 CFR 96.8 - Fees charged by accrediting entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Fees charged by accrediting entities. 96.8... Duties of Accrediting Entities § 96.8 Fees charged by accrediting entities. (a) An accrediting entity may... fees approved by the Secretary. Before approving a schedule of fees proposed by an accrediting entity...

42 CFR 417.484 - Requirement applicable to related entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 3 2011-10-01 2011-10-01 false Requirement applicable to related entities. 417.484... entities. (a) Definition. As used in this section, related entity means any entity that is related to the... agrees to require all related entities to agree that— (1) HHS, the Comptroller General, or their...

42 CFR 417.484 - Requirement applicable to related entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Requirement applicable to related entities. 417.484... entities. (a) Definition. As used in this section, related entity means any entity that is related to the... agrees to require all related entities to agree that— (1) HHS, the Comptroller General, or their...

17 CFR 45.6 - Legal entity identifiers

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 2 2014-04-01 2014-04-01 false Legal entity identifiers 45.6... RECORDKEEPING AND REPORTING REQUIREMENTS § 45.6 Legal entity identifiers Each counterparty to any swap subject... reporting pursuant to this part by means of a single legal entity identifier as specified in this section...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Iran; Iranian Entity. 535.301 Section 535.301 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Iran; Iranian Entity. 535.301 Section 535.301 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the...

[Face recognition in patients with autism spectrum disorders].

PubMed

Kita, Yosuke; Inagaki, Masumi

2012-07-01

The present study aimed to review previous research conducted on face recognition in patients with autism spectrum disorders (ASD). Face recognition is a key question in the ASD research field because it can provide clues for elucidating the neural substrates responsible for the social impairment of these patients. Historically, behavioral studies have reported low performance and/or unique strategies of face recognition among ASD patients. However, the performance and strategy of ASD patients is comparable to those of the control group, depending on the experimental situation or developmental stage, suggesting that face recognition of ASD patients is not entirely impaired. Recent brain function studies, including event-related potential and functional magnetic resonance imaging studies, have investigated the cognitive process of face recognition in ASD patients, and revealed impaired function in the brain's neural network comprising the fusiform gyrus and amygdala. This impaired function is potentially involved in the diminished preference for faces, and in the atypical development of face recognition, eliciting symptoms of unstable behavioral characteristics in these patients. Additionally, face recognition in ASD patients is examined from a different perspective, namely self-face recognition, and facial emotion recognition. While the former topic is intimately linked to basic social abilities such as self-other discrimination, the latter is closely associated with mentalizing. Further research on face recognition in ASD patients should investigate the connection between behavioral and neurological specifics in these patients, by considering developmental changes and the spectrum clinical condition of ASD.

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 31 Money and Finance:Treasury 3 2013-07-01 2013-07-01 false Iran; Iranian Entity. 535.301 Section... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the Government of Iran as well as any political subdivision, agency, or instrumentality thereof or any territory...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 31 Money and Finance:Treasury 3 2012-07-01 2012-07-01 false Iran; Iranian Entity. 535.301 Section... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the Government of Iran as well as any political subdivision, agency, or instrumentality thereof or any territory...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 31 Money and Finance:Treasury 3 2014-07-01 2014-07-01 false Iran; Iranian Entity. 535.301 Section... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the Government of Iran as well as any political subdivision, agency, or instrumentality thereof or any territory...

Visual discrimination predicts naming and semantic association accuracy in Alzheimer disease.

PubMed

Harnish, Stacy M; Neils-Strunjas, Jean; Eliassen, James; Reilly, Jamie; Meinzer, Marcus; Clark, John Greer; Joseph, Jane

2010-12-01

Language impairment is a common symptom of Alzheimer disease (AD), and is thought to be related to semantic processing. This study examines the contribution of another process, namely visual perception, on measures of confrontation naming and semantic association abilities in persons with probable AD. Twenty individuals with probable mild-moderate Alzheimer disease and 20 age-matched controls completed a battery of neuropsychologic measures assessing visual perception, naming, and semantic association ability. Visual discrimination tasks that varied in the degree to which they likely accessed stored structural representations were used to gauge whether structural processing deficits could account for deficits in naming and in semantic association in AD. Visual discrimination abilities of nameable objects in AD strongly predicted performance on both picture naming and semantic association ability, but lacked the same predictive value for controls. Although impaired, performance on visual discrimination tests of abstract shapes and novel faces showed no significant relationship with picture naming and semantic association. These results provide additional evidence to support that structural processing deficits exist in AD, and may contribute to object recognition and naming deficits. Our findings suggest that there is a common deficit in discrimination of pictures using nameable objects, picture naming, and semantic association of pictures in AD. Disturbances in structural processing of pictured items may be associated with lexical-semantic impairment in AD, owing to degraded internal storage of structural knowledge.

Online recognition of Chinese characters: the state-of-the-art.

PubMed

Liu, Cheng-Lin; Jaeger, Stefan; Nakagawa, Masaki

2004-02-01

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

14 CFR Sec. 1-6 - Accounting entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 4 2011-01-01 2011-01-01 false Accounting entities. Sec. 1-6 Section 1-6... Provisions Sec. 1-6 Accounting entities. (a) Separate accounting records shall be maintained for each air transport entity for which separate reports to the BTS are required to be made by sections 21(g) and for...

Recognition of Telugu characters using neural networks.

PubMed

Sukhaswami, M B; Seetharamulu, P; Pujari, A K

1995-09-01

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

Eye Movements to Pictures Reveal Transient Semantic Activation during Spoken Word Recognition

ERIC Educational Resources Information Center

Yee, Eiling; Sedivy, Julie C.

2006-01-01

Two experiments explore the activation of semantic information during spoken word recognition. Experiment 1 shows that as the name of an object unfolds (e.g., lock), eye movements are drawn to pictorial representations of both the named object and semantically related objects (e.g., key). Experiment 2 shows that objects semantically related to an…

Serial position effects in recognition memory for odors: a reexamination.

PubMed

Miles, Christopher; Hodder, Kathryn

2005-10-01

Seven experiments examined recognition memory for sequentially presented odors. Following Reed (2000), participants were presented with a sequence of odors and then required to identify an odor from the sequence in a test probe comprising 2 odors. The pattern of results obtained by Reed (2000, although statistically marginal) demonstrated enhanced recognition for odors presented at the start (primacy) and end (recency) of the sequence: a result that we failed to replicate in any of the experiments reported here. Experiments 1 and 3 were designed to replicate Reed (2000), employing five-item and seven-item sequences, respectively, and each demonstrated significant recency, with evidence of primacy in Experiment 3 only. Experiment 2 replicated Experiment 1, with reduced interstimulus intervals, and produced a null effect of serial position. The ease with which the odors could be verbally labeled was manipulated in Experiments 4 and 5. Nameable odors produced a null effect of serial position (Experiment 4), and hard-to-name odors produced a pronounced recency effect (Experiment 5); nevertheless, overall rates of recognition were remarkably similar for the two experiments at around 70%. Articulatory suppression reduced recognition accuracy (Experiment 6), but recency was again present in the absence of primacy. Odor recognition performance was immune to the effects of an interleaved odor (Experiment 7), and, again, both primacy and recency effects were absent. There was no evidence of olfactory fatigue: Recognition accuracy improved across trials (Experiment 1). It is argued that the results of the experiments reported here are generally consistent with that body of work employing hard-to-name visual stimuli, where recency is obtained in the absence of primacy when the retention interval is short.

Cross-modal pattern of brain activations associated with the processing of self- and significant other's name.

PubMed

Tacikowski, Pawel; Brechmann, André; Nowicka, Anna

2013-09-01

Previous neuroimaging studies have shown that the patterns of brain activity during the processing of personally relevant names (e.g., own name, friend's name, partner's name, etc.) and the names of famous people (e.g., celebrities) are different. However, it is not known how the activity in this network is influenced by the modality of the presented stimuli. In this fMRI study, we investigated the pattern of brain activations during the recognition of aurally and visually presented full names of the subject, a significant other, a famous person and unknown individuals. In both modalities, we found that the processing of self-name and the significant other's name was associated with increased activation in the medial prefrontal cortex (MPFC). Acoustic presentations of these names also activated bilateral inferior frontal gyri (IFG). This pattern of results supports the role of MPFC in the processing of personally relevant information, irrespective of their modality. Copyright © 2012 Wiley Periodicals, Inc., a Wiley company.

47 CFR 27.1218 - Designated entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 2 2014-10-01 2014-10-01 false Designated entities. 27.1218 Section 27.1218 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES Broadband Radio Service and Educational Broadband Service § 27.1218 Designated entities...

47 CFR 27.1218 - Designated entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 2 2013-10-01 2013-10-01 false Designated entities. 27.1218 Section 27.1218 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES Broadband Radio Service and Educational Broadband Service § 27.1218 Designated entities...

Package Design Affects Accuracy Recognition for Medications.

PubMed

Endestad, Tor; Wortinger, Laura A; Madsen, Steinar; Hortemo, Sigurd

2016-12-01

Our aim was to test if highlighting and placement of substance name on medication package have the potential to reduce patient errors. An unintentional overdose of medication is a large health issue that might be linked to medication package design. In two experiments, placement, background color, and the active ingredient of generic medication packages were manipulated according to best human factors guidelines to reduce causes of labeling-related patient errors. In two experiments, we compared the original packaging with packages where we varied placement of the name, dose, and background of the active ingredient. Age-relevant differences and the effect of color on medication recognition error were tested. In Experiment 1, 59 volunteers (30 elderly and 29 young students), participated. In Experiment 2, 25 volunteers participated. The most common error was the inability to identify that two different packages contained the same active ingredient (young, 41%, and elderly, 68%). This kind of error decreased with the redesigned packages (young, 8%, and elderly, 16%). Confusion errors related to color design were reduced by two thirds in the redesigned packages compared with original generic medications. Prominent placement of substance name and dose with a band of high-contrast color support recognition of the active substance in medications. A simple modification including highlighting and placing the name of the active ingredient in the upper right-hand corner of the package helps users realize that two different packages can contain the same active substance, thus reducing the risk of inadvertent medication overdose. © 2016, Human Factors and Ergonomics Society.

Package Design Affects Accuracy Recognition for Medications

PubMed Central

Endestad, Tor; Wortinger, Laura A.; Madsen, Steinar; Hortemo, Sigurd

2016-01-01

Objective: Our aim was to test if highlighting and placement of substance name on medication package have the potential to reduce patient errors. Background: An unintentional overdose of medication is a large health issue that might be linked to medication package design. In two experiments, placement, background color, and the active ingredient of generic medication packages were manipulated according to best human factors guidelines to reduce causes of labeling-related patient errors. Method: In two experiments, we compared the original packaging with packages where we varied placement of the name, dose, and background of the active ingredient. Age-relevant differences and the effect of color on medication recognition error were tested. In Experiment 1, 59 volunteers (30 elderly and 29 young students), participated. In Experiment 2, 25 volunteers participated. Results: The most common error was the inability to identify that two different packages contained the same active ingredient (young, 41%, and elderly, 68%). This kind of error decreased with the redesigned packages (young, 8%, and elderly, 16%). Confusion errors related to color design were reduced by two thirds in the redesigned packages compared with original generic medications. Conclusion: Prominent placement of substance name and dose with a band of high-contrast color support recognition of the active substance in medications. Application: A simple modification including highlighting and placing the name of the active ingredient in the upper right-hand corner of the package helps users realize that two different packages can contain the same active substance, thus reducing the risk of inadvertent medication overdose. PMID:27591209

Face recognition using slow feature analysis and contourlet transform

NASA Astrophysics Data System (ADS)

Wang, Yuehao; Peng, Lingling; Zhe, Fuchuan

2018-04-01

In this paper we propose a novel face recognition approach based on slow feature analysis (SFA) in contourlet transform domain. This method firstly use contourlet transform to decompose the face image into low frequency and high frequency part, and then takes technological advantages of slow feature analysis for facial feature extraction. We named the new method combining the slow feature analysis and contourlet transform as CT-SFA. The experimental results on international standard face database demonstrate that the new face recognition method is effective and competitive.

78 FR 22270 - Special Fraud Alert: Physician-Owned Entities

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-15

...] Special Fraud Alert: Physician-Owned Entities AGENCY: Office of Inspector General (OIG), HHS. ACTION... Physician-Owned Entities. Specifically, the Special Fraud Alert addressed physician-owned entities that... publication of the Special Fraud Alert on Physician-Owned Entities, an inadvertent error appeared in the DATES...

Speech-recognition interfaces for music information retrieval

NASA Astrophysics Data System (ADS)

Goto, Masataka

2005-09-01

This paper describes two hands-free music information retrieval (MIR) systems that enable a user to retrieve and play back a musical piece by saying its title or the artist's name. Although various interfaces for MIR have been proposed, speech-recognition interfaces suitable for retrieving musical pieces have not been studied. Our MIR-based jukebox systems employ two different speech-recognition interfaces for MIR, speech completion and speech spotter, which exploit intentionally controlled nonverbal speech information in original ways. The first is a music retrieval system with the speech-completion interface that is suitable for music stores and car-driving situations. When a user only remembers part of the name of a musical piece or an artist and utters only a remembered fragment, the system helps the user recall and enter the name by completing the fragment. The second is a background-music playback system with the speech-spotter interface that can enrich human-human conversation. When a user is talking to another person, the system allows the user to enter voice commands for music playback control by spotting a special voice-command utterance in face-to-face or telephone conversations. Experimental results from use of these systems have demonstrated the effectiveness of the speech-completion and speech-spotter interfaces. (Video clips: http://staff.aist.go.jp/m.goto/MIR/speech-if.html)

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

PubMed Central

Wu, Jei-Tun

2016-01-01

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. Usually, only one of those tasks is chosen to examine lexical access in a study. Using two exemplar experiments, this paper introduces an approach to include both the lexical decision task and the naming task in a study. In the first experiment, the stimuli were Chinese characters with frequency and regularity manipulated. In the second experiment, the stimuli were switched to Chinese two-character words, in which the word frequency and the regularity of the leading character were manipulated. The logic of these two exemplar experiments was to explore some important issues such as the role of phonology on recognition by comparing the frequency effect between both the tasks. The results revealed different patterns of lexical access from those reported in the alphabetic systems. The results of Experiment 1 manifested a larger frequency effect in the naming task as compared to the LDT, when the stimuli were Chinese characters. And it is noteworthy that, in Experiment 1, when the stimuli were regular Chinese characters, the frequency effect observed in the naming task was roughly equivalent to that in the LDT. However, a smaller frequency effect was shown in the naming task as compared to the LDT, when the stimuli were switched to Chinese two-character words in Experiment 2. Taking advantage of the respective demands and characteristics in both tasks, researchers can obtain a more complete and precise picture of character/word recognition. PMID:27077703

A GPU-paralleled implementation of an enhanced face recognition algorithm

NASA Astrophysics Data System (ADS)

Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo

2013-03-01

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

Processing new and repeated names: Effects of coreference on repetition priming with speech and fast RSVP

PubMed Central

Camblin, C. Christine; Ledoux, Kerry; Boudewyn, Megan; Gordon, Peter C.; Swaab, Tamara Y.

2006-01-01

Previous research has shown that the process of establishing coreference with a repeated name can affect basic repetition priming. Specifically, repetition priming on some measures can be eliminated for repeated names that corefer with an entity that is prominent in the discourse model. However, the exact nature and timing of this modulating effect of discourse are not yet understood. Here, we present two ERP studies that further probe the nature of repeated name coreference by using naturally produced connected speech and fast-rate RSVP methods of presentation. With speech we found that repetition priming was eliminated for repeated names that coreferred with a prominent antecedent. In contrast, with fast-rate RSVP, we found a main effect of repetition that did not interact with sentence context. This indicates that the creation of a discourse model during comprehension can affect repetition priming, but the nature of this effect may depend on input speed. PMID:16904078

The use and limits of scientific names in biological informatics.

PubMed

Remsen, David

2016-01-01

Scientific names serve to label biodiversity information: information related to species. Names, and their underlying taxonomic definitions, however, are unstable and ambiguous. This negatively impacts the utility of names as identifiers and as effective indexing tools in biological informatics where names are commonly utilized for searching, retrieving and integrating information about species. Semiotics provides a general model for describing the relationship between taxon names and taxon concepts. It distinguishes syntactics, which governs relationships among names, from semantics, which represents the relations between those labels and the taxa to which they refer. In the semiotic context, changes in semantics (i.e., taxonomic circumscription) do not consistently result in a corresponding and reflective change in syntax. Further, when syntactic changes do occur, they may be in response to semantic changes or in response to syntactic rules. This lack of consistency in the cardinal relationship between names and taxa places limits on how scientific names may be used in biological informatics in initially anchoring, and in the subsequent retrieval and integration, of relevant biodiversity information. Precision and recall are two measures of relevance. In biological taxonomy, recall is negatively impacted by changes or ambiguity in syntax while precision is negatively impacted when there are changes or ambiguity in semantics. Because changes in syntax are not correlated with changes in semantics, scientific names may be used, singly or conflated into synonymous sets, to improve recall in pattern recognition or search and retrieval. Names cannot be used, however, to improve precision. This is because changes in syntax do not uniquely identify changes in circumscription. These observations place limits on the utility of scientific names within biological informatics applications that rely on names as identifiers for taxa. Taxonomic systems and services used to

Age effects on visual-perceptual processing and confrontation naming.

PubMed

Gutherie, Audrey H; Seely, Peter W; Beacham, Lauren A; Schuchard, Ronald A; De l'Aune, William A; Moore, Anna Bacon

2010-03-01

The impact of age-related changes in visual-perceptual processing on naming ability has not been reported. The present study investigated the effects of 6 levels of spatial frequency and 6 levels of contrast on accuracy and latency to name objects in 14 young and 13 older neurologically normal adults with intact lexical-semantic functioning. Spatial frequency and contrast manipulations were made independently. Consistent with the hypotheses, variations in these two visual parameters impact naming ability in young and older subjects differently. The results from the spatial frequency-manipulations revealed that, in general, young vs. older subjects are faster and more accurate to name. However, this age-related difference is dependent on the spatial frequency on the image; differences were only seen for images presented at low (e.g., 0.25-1 c/deg) or high (e.g., 8-16 c/deg) spatial frequencies. Contrary to predictions, the results from the contrast manipulations revealed that overall older vs. young adults are more accurate to name. Again, however, differences were only seen for images presented at the lower levels of contrast (i.e., 1.25%). Both age groups had shorter latencies on the second exposure of the contrast-manipulated images, but this possible advantage of exposure was not seen for spatial frequency. Category analyses conducted on the data from this study indicate that older vs. young adults exhibit a stronger nonliving-object advantage for naming spatial frequency-manipulated images. Moreover, the findings suggest that bottom-up visual-perceptual variables integrate with top-down category information in different ways. Potential implications on the aging and naming (and recognition) literature are discussed.

14 CFR 1-6 - Accounting entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 4 2012-01-01 2012-01-01 false Accounting entities. Sec. 1-6 Section Sec. 1-6 Aeronautics and Space OFFICE OF THE SECRETARY, DEPARTMENT OF TRANSPORTATION (AVIATION... General Accounting Provisions Sec. 1-6 Accounting entities. (a) Separate accounting records shall be...

46 CFR 403.110 - Accounting entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Accounting entities. 403.110 Section 403.110 Shipping COAST GUARD (GREAT LAKES PILOTAGE), DEPARTMENT OF HOMELAND SECURITY GREAT LAKES PILOTAGE UNIFORM ACCOUNTING SYSTEM General § 403.110 Accounting entities. Each Association shall be a separate accounting...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and Diabetes Outcome Measurements § 410.145 Requirements for entities. (a) Deemed entities. (1) Except as...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and Diabetes Outcome Measurements § 410.145 Requirements for entities. (a) Deemed entities. (1) Except as...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2012 CFR

2012-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and Diabetes Outcome Measurements § 410.145 Requirements for entities. (a) Deemed entities. (1) Except as...

Finding needles in haystacks: linking scientific names, reference specimens and molecular data for Fungi

Treesearch

C.L. Schoch; B. Robbertse; V. Robert; R.G. Haight; K. Kovacs; B. Leung; W. Meyer; R.H. Nilsson; K. Hughes; A.N. Miller; P.M. Kirk; K. Abarenkov; M.C. Aime; H.A. Ariyawansa; M. Bidartondo; T. Boekhout; B. Buyck; Q. Cai; J. Chen; A. Crespo; P.W. Crous; U. Damm; Z.W. De Beer; B.T.M. Dentinger; P.K. Divakar; M. Duenas; N. Feau; K. Fliegerova; M.A. Garcia; Z.-W. Ge; G.W. Griffith; J.Z. Groenewald; M. Groenewald; M. Grube; M. Gryzenhout; C. Gueidan; L. Guo; S. Hambleton; R. Hamelin; K. Hansen; V. Hofstetter; S.-B. Hong; J. Houbraken; K.D. Hyde; P. Inderbitzin; P.R. Johnston; S.C. Karunarathna; U. Koljalg; G.M. Kovacs; E. Kraichak; K. Krizsan; C.P. Kurtzman; K.-H. Larsson; S. Leavitt; P.M. Letcher; K. Liimatainen; J.-K. Liu; D.J. Lodge; J. Jennifer Luangsa-ard; H.T. Lumbsch; S.S.N. Maharachchikumbura; D. Manamgoda; M.P. Martin; A.M. Minnis; J.-M. Moncalvo; G. Mule; K.K. Nakasone; T. Niskanen; I. Olariaga; T. Papp; T. Petkovits; R. Pino-Bodas; M.J. Powell; H.A. Raja; D. Redecker; J.M. Sarmiento-Ramirez; K.A. Seifert; B. Shrestha; S. Stenroos; B. Stielow; S.-O. Suh; K. Tanaka; L. Tedersoo; M.T. Telleria; D. Udayanga; W.A. Untereiner; J. Dieguez Uribeondo; K.V. Subbarao; C. Vagvolgyi; C. Visagie; K. Voigt; D.M. Walker; B.S. Weir; M. Weiss; N.N. Wijayawardene; M.J. Wingfield; J.P. Xu; Z.L. Yang; N. Zhang; W.-Y. Zhuang; S. Federhen

2014-01-01

DNA phylogenetic comparisons have shown that morphology-based species recognition often underestimates fungal diversity. Therefore, the need for accurate DNA sequence data, tied to both correct taxonomic names and clearly annotated specimen data, has never been greater. Furthermore, the growing number of molecular ecology and microbiome projects using high-throughput...

Long-term effect of a name change for schizophrenia on reducing stigma.

PubMed

Koike, Shinsuke; Yamaguchi, Sosei; Ojio, Yasutaka; Shimada, Takafumi; Watanabe, Kei-ichiro; Ando, Shuntaro

2015-10-01

A name change for schizophrenia was first implemented in Japan for reducing stigma in 2002; however, little is known of its long-term impact. Total 259 students from 20 universities answered an anonymous self-administered questionnaire about their mental health-related experiences, and stigma scales including feasible knowledge and negative stereotypes for four specific diseases, including schizophrenia (old and new names), depression, and diabetes mellitus. We also asked to choose the old and new names of schizophrenia and dementia among ten names for mental and physical illnesses and conditions. The participants had more feasible knowledge and fewer negative stereotypes for the new name of schizophrenia than the old name, but were still significantly worse than for depression and diabetes mellitus (p < 0.01). Direct contact experiences with those who have mental health problems were associated with feasible knowledge for schizophrenia but not negative stereotypes (β = 0.13, p = 0.020). The rate of correct responses for the old and new names of schizophrenia was significantly lower than that of dementia (41 vs. 87%, p < 0.001). Mental health-related experience from media was associated with the recognition of name change for schizophrenia (p = 0.008), which was associated with less feasible knowledge for new name of schizophrenia. The name change of schizophrenia has reduced stigma since 12 years have passed. More effective campaigns, educational curricula, and policy making are needed to reduce stigma toward schizophrenia.

17 CFR 202.8 - Small entity compliance guides.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false Small entity compliance guides. 202.8 Section 202.8 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION INFORMAL AND OTHER PROCEDURES § 202.8 Small entity compliance guides. The following small entity compliance guides...

17 CFR 202.8 - Small entity compliance guides.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false Small entity compliance guides. 202.8 Section 202.8 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION INFORMAL AND OTHER PROCEDURES § 202.8 Small entity compliance guides. The following small entity compliance guides...

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

What Is in the Naming? A 5-Year Longitudinal Study of Early Rapid Naming and Phonological Sensitivity in Relation to Subsequent Reading Skills in Both Native Chinese and English as a Second Language

ERIC Educational Resources Information Center

Pan, Jinger; McBride-Chang, Catherine; Shu, Hua; Liu, Hongyun; Zhang, Yuping; Li, Hong

2011-01-01

Among 262 Chinese children, syllable awareness and rapid automatized naming (RAN) at age 5 years and invented spelling of Pinyin at age 6 years independently predicted subsequent Chinese character recognition and English word reading at ages 8 years and 10 years, even with initial Chinese character reading ability statistically controlled. In…

31 CFR 537.312 - Nongovernmental entity in Burma.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Nongovernmental entity in Burma. 537.312 Section 537.312 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued... Definitions § 537.312 Nongovernmental entity in Burma. The term nongovernmental entity in Burma means a...

31 CFR 537.312 - Nongovernmental entity in Burma.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Nongovernmental entity in Burma. 537.312 Section 537.312 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued... Definitions § 537.312 Nongovernmental entity in Burma. The term nongovernmental entity in Burma means a...

The first does the work, but the third time's the charm: the effects of massed repetition on episodic encoding of multimodal face-name associations.

PubMed

Mangels, Jennifer A; Manzi, Alberto; Summerfield, Christopher

2010-03-01

In social interactions, it is often necessary to rapidly encode the association between visually presented faces and auditorily presented names. The present study used event-related potentials to examine the neural correlates of associative encoding for multimodal face-name pairs. We assessed study-phase processes leading to high-confidence recognition of correct pairs (and consistent rejection of recombined foils) as compared to lower-confidence recognition of correct pairs (with inconsistent rejection of recombined foils) and recognition failures (misses). Both high- and low-confidence retrieval of face-name pairs were associated with study-phase activity suggestive of item-specific processing of the face (posterior inferior temporal negativity) and name (fronto-central negativity). However, only those pairs later retrieved with high confidence recruited a sustained centro-parietal positivity that an ancillary localizer task suggested may index an association-unique process. Additionally, we examined how these processes were influenced by massed repetition, a mnemonic strategy commonly employed in everyday situations to improve face-name memory. Differences in subsequent memory effects across repetitions suggested that associative encoding was strongest at the initial presentation, and thus, that the initial presentation has the greatest impact on memory formation. Yet, exploratory analyses suggested that the third presentation may have benefited later memory by providing an opportunity for extended processing of the name. Thus, although encoding of the initial presentation was critical for establishing a strong association, the extent to which processing was sustained across subsequent immediate (massed) presentations may provide additional encoding support that serves to differentiate face-name pairs from similar (recombined) pairs by providing additional encoding opportunities for the less dominant stimulus dimension (i.e., name).

26 CFR 301.7701-5 - Domestic and foreign business entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 18 2010-04-01 2010-04-01 false Domestic and foreign business entities. 301... foreign business entities. (a) Domestic and foreign business entities. A business entity (including an entity that is disregarded as separate from its owner under § 301.7701-2(c)) is domestic if it is created...

26 CFR 301.7701-5 - Domestic and foreign business entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 18 2011-04-01 2011-04-01 false Domestic and foreign business entities. 301... foreign business entities. (a) Domestic and foreign business entities. A business entity (including an entity that is disregarded as separate from its owner under § 301.7701-2(c)) is domestic if it is created...

HWDA: A coherence recognition and resolution algorithm for hybrid web data aggregation

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Jian; Wang, Tong

2017-09-01

Aiming at the object confliction recognition and resolution problem for hybrid distributed data stream aggregation, a distributed data stream object coherence solution technology is proposed. Firstly, the framework was defined for the object coherence conflict recognition and resolution, named HWDA. Secondly, an object coherence recognition technology was proposed based on formal language description logic and hierarchical dependency relationship between logic rules. Thirdly, a conflict traversal recognition algorithm was proposed based on the defined dependency graph. Next, the conflict resolution technology was prompted based on resolution pattern matching including the definition of the three types of conflict, conflict resolution matching pattern and arbitration resolution method. At last, the experiment use two kinds of web test data sets to validate the effect of application utilizing the conflict recognition and resolution technology of HWDA.

The involvement of emotion recognition in affective theory of mind.

PubMed

Mier, Daniela; Lis, Stefanie; Neuthe, Kerstin; Sauer, Carina; Esslinger, Christine; Gallhofer, Bernd; Kirsch, Peter

2010-11-01

This study was conducted to explore the relationship between emotion recognition and affective Theory of Mind (ToM). Forty subjects performed a facial emotion recognition and an emotional intention recognition task (affective ToM) in an event-related fMRI study. Conjunction analysis revealed overlapping activation during both tasks. Activation in some of these conjunctly activated regions was even stronger during affective ToM than during emotion recognition, namely in the inferior frontal gyrus, the superior temporal sulcus, the temporal pole, and the amygdala. In contrast to previous studies investigating ToM, we found no activation in the anterior cingulate, commonly assumed as the key region for ToM. The results point to a close relationship of emotion recognition and affective ToM and can be interpreted as evidence for the assumption that at least basal forms of ToM occur by an embodied, non-cognitive process. Copyright © 2010 Society for Psychophysiological Research.

26 CFR 1.892-5 - Controlled commercial entity.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 9 2010-04-01 2010-04-01 false Controlled commercial entity. 1.892-5 Section 1... (CONTINUED) INCOME TAXES Miscellaneous Provisions § 1.892-5 Controlled commercial entity. (a)-(a)(2...)(B), the term entity means and includes a corporation, a partnership, a trust (including a pension...