1. Articles in category: WSD

    25-48 of 354 « 1 2 3 4 5 ... 13 14 15 »
    1. Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.

      Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.

      AMIA Annu Symp Proc. 2013;2013:1007-16

      Authors: Moon S, Berster BT, Xu H, Cohen T

      Abstract Automated Word Sense Disambiguation in clinical documents is a prerequisite to accurate extraction of medical information. Emerging methods utilizing hyperdimensional computing present new approaches to this problem.

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    2. Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods.

      Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods.

      J Am Med Inform Assoc. 2014 Jan 17;

      Authors: Chasin R, Rumshisky A, Uzuner O, Szolovits P

      Abstract OBJECTIVE: To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches.

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    3. Applying active learning to supervised word sense disambiguation in MEDLINE.

      Applying active learning to supervised word sense disambiguation in MEDLINE.

      J Am Med Inform Assoc. 2013 Sep-Oct;20(5):1001-6

      Authors: Chen Y, Cao H, Mei Q, Zheng K, Xu H

      Abstract OBJECTIVES: This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models.

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      Mentions: Medline SVM Msh Wsd
    4. Determining the Difficulty of Word Sense Disambiguation.

      Determining the Difficulty of Word Sense Disambiguation.

      J Biomed Inform. 2013 Sep 25;

      Authors: McInnes B, Stevenson M

      Abstract Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning.

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    5. Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text.

      Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text.

      J Biomed Inform. 2013 Sep 3;

      Authors: McInnes B, Pedersen T

      Abstract INTRODUCTION: In this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures.

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    6. Natural language hypernym weighting for word sense disambiguation

      Technologies are described herein for probabilistically assigning weights to word senses and hypernyms of a word. The weights can be used in natural language processing applications such as information indexing and querying. A word hypernym weight (WHW) score can be determined by summing word sense probabilities of word senses from which the hypernym is inherited. WHW scores can be used to prune away hypernyms prior to indexing, to rank query results, and for other functions related to information indexing and querying. A semantic search technique can use WHW scores to retrieve an entry related to a word from an index ...
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    7. Semantic similarity in the biomedical domain: an evaluation across knowledge sources.

      Related Articles Semantic similarity in the biomedical domain: an evaluation across knowledge sources. BMC Bioinformatics. 2012;13:261 Authors: Garla VN, Brandt C Abstract BACKGROUND: Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within ...
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    8. Knowledge-based method for determining the meaning of ambiguous biomedical terms using information content measures of similarity.

      Knowledge-based method for determining the meaning of ambiguous biomedical terms using information content measures of similarity.
      Related Articles Knowledge-based method for determining the meaning of ambiguous biomedical terms using information content measures of similarity. AMIA Annu Symp Proc. 2011;2011:895-904 Authors: McInnes BT, Pedersen T, Liu Y, Melton GB, Pakhomov SV Abstract In this paper, we introduce a novel knowledge-based word sense disambiguation method that determines the sense of an ambiguous word in biomedical text using semantic similarity or relatedness measures. These measures quantify the degree of similarity between concepts in the Unified Medical Language System (UMLS). The objective of this work was to develop a method that can disambiguate terms in biomedical text by ...
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    9. Combining Corpus-derived Sense Profiles with Estimated Frequency Information to Disambiguate Clinical Abbreviations.

      Combining Corpus-derived Sense Profiles with Estimated Frequency Information to Disambiguate Clinical Abbreviations. AMIA Annu Symp Proc. 2012;2012:1004-13 Authors: Xu H, Stetson PD, Friedman C Abstract Abbreviations are widely used in clinical notes and are often ambiguous. Word sense disambiguation (WSD) for clinical abbreviations therefore is a critical task for many clinical natural language processing (NLP) systems. Supervised machine learning based WSD methods are known for their high performance. However, it is time consuming and costly to construct annotated samples for supervised WSD approaches and sense frequency information is often ignored by these methods. In this study, we proposed ...
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    10. Hyperdimensional computing approach to word sense disambiguation.

      Hyperdimensional computing approach to word sense disambiguation. AMIA Annu Symp Proc. 2012;2012:1129-38 Authors: Berster BT, Goodwin JC, Cohen T Abstract Coping with the ambiguous meanings of words has long been a hurdle for information retrieval and natural language processing systems. This paper presents a new word sense disambiguation approach using high-dimensional binary vectors, which encode meanings of words based on the different contexts in which they occur. In our approach, a randomly constructed vector is assigned to each ambiguous term, and another to each sense of this term. In the context of a sense-annotated training set, a reversible ...
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    11. Method, system and apparatus for automatic keyword extraction

      The present invention provides a method and a system for automatic keyword extraction based on supervised or unsupervised machine learning techniques. Novel linguistically-motivated machine learning features are introduced, including discourse comprehension features based on construction integration theory, numeric features making use of syntactic part-of-speech patterns, and probabilistic features based on analysis of online encyclopedia annotations. The improved keyword extraction methods are combined with word sense disambiguation into a system for automatically generating annotations to enrich text with links to encyclopedic knowledge.
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    12. The cross-lingual lexical substitution task

      The cross-lingual lexical substitution task
      Abstract  In this paper we provide an account of the cross-lingual lexical substitution task run as part of SemEval-2010. In this task both annotators (native Spanish speakers, proficient in English) and participating systems had to find Spanish translations for target words in the context of an English sentence. Because only translations of a single lexical unit were required, this task does not necessitate a full blown translation system. This we hope encouraged those working specifically on lexical semantics to participate without a requirement for them to use machine translation software, though they were free to use whatever resources they chose ...
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    13. Multi-view constrained clustering with an incomplete mapping between views

      Multi-view constrained clustering with an incomplete mapping between views
      Abstract  Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via ...
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    14. A Classification Model with Corpus Enrichment for Toponym Disambiguation

      This paper presents a method based on information retrieval to enrich corpus using bootstrapping techniques. A supervised corpus manually validated is provided, and then snippets are obtained from Web in order to increase the size of the initial corpus. Although this technique has already been reported in the literature, the main objective of this work is to apply it under the specific task of GEO/NO-GEO toponym disambiguation.The disambiguation procedure is evaluated by a classification model observing favorable results. Content Type Book ChapterPages 472-480DOI 10.1007/978-3-642-34654-5_48Authors Belém Priego Sánchez, FCC, Benemérita Universidad Autónoma de Puebla, Av. San Claudio ...
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    15. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation

      The Naïve Bayes Model for Unsupervised Word Sense Disambiguation Content Type BookPublisher Springer Berlin HeidelbergDOI 10.1007/978-3-642-33693-5Copyright 2013ISBN 978-3-642-33692-8 (Print) 978-3-642-33693-5 (Online)Authors Florentina T. Hristea, Faculty of Mathematics & Computer Scienc, Department of Computer Science, University of Bucharest, Emil Racovita 12, Bucharest, 041758 Romania Book Series SpringerBriefs in StatisticsOnline ISSN 2191-5458Print ISSN 2191-544X
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    16. Preliminaries

      Preliminaries
      This chapter describes the problem we are investigating and trying to solve in all other chapters. It introduces word sense disambiguation (WSD) and Naïve Bayes-based WSD, as well as local type features for unsupervised WSD with an underlying Naïve Bayes model. Content Type Book ChapterPages 1-8DOI 10.1007/978-3-642-33693-5_1Authors Florentina T. Hristea, Faculty of Mathematics and Computer Science, Department of Computer Science, University of Bucharest, Emil Racovita 12, 041758 Bucharest, Romania Book Series SpringerBriefs in StatisticsOnline ISSN 2191-5458Print ISSN 2191-544X Book The Naïve Bayes Model for Unsupervised Word Sense DisambiguationDOI 10.1007/978-3-642-33693-5Online ISBN 978-3-642-33693-5Print ISBN 978-3-642-33692-8
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    17. N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model

      N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model
      The feature selection method we are presenting in this chapter relies on web scale N-gram counts. It uses counts collected from the web in order to rank candidates. Features are thus created from unlabeled data, a strategy which is part of a growing trend in natural language processing. Disambiguation results obtained by web N-gram feature selection will be compared to those of previous approaches that equally rely on an underlying Naïve Bayes model but on completely different feature sets. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will show that web N-gram feature selection for the ...
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    18. The Naïve Bayes Model in the Context of Word Sense Disambiguation

      This chapter discusses the Naïve Bayes model strictly in the context of word sense disambiguation. The theoretical model is presented and its implementation is discussed. Special attention is paid to parameter estimation and to feature selection, the two main issues of the model’s implementation. The EM algorithm is recommended as suitable for parameter estimation in the case of unsupervised WSD. Feature selection will be surveyed in the following chapters. Content Type Book ChapterPages 9-16DOI 10.1007/978-3-642-33693-5_2Authors Florentina T. Hristea, Faculty of Mathematics and Computer Science, Department of Computer Science, University of Bucharest, Emil Racovita 12, 041758 Bucharest, Romania ...
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    19. N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model

      The feature selection method we are presenting in this chapter relies on web scale N-gram counts. It uses counts collected from the web in order to rank candidates. Features are thus created from unlabeled data, a strategy which is part of a growing trend in natural language processing. Disambiguation results obtained by web N-gram feature selection will be compared to those of previous approaches that equally rely on an underlying Naïve Bayes model but on completely different feature sets. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will show that web N-gram feature selection for the ...
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    20. Semantic WordNet-Based Feature Selection

      The feature selection method we are presenting in this chapter makes use of the semantic network WordNet as knowledge source for feature selection. The method makes ample use of the WordNet semantic relations which are typical of each part of speech, thus placing the disambiguation process at the border between unsupervised and knowledge-based techniques. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will be compared to previously existing disambiguation results, obtained when performing a completely different type of feature selection. Our main conclusion will be that the Naïve Bayes model reacts well in the presence of ...
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    21. Syntactic Dependency-Based Feature Selection

      The feature selection method we are presenting in this chapter makes use of syntactic knowledge provided by dependency relations. Dependency-based feature selection for the Naïve Bayes model is examined and exemplified in the case of adjectives. Performing this type of knowledge-based feature selection places the disambiguation process at the border between unsupervised and knowledge-based techniques. The discussed type of feature selection and corresponding disambiguation method will once again prove that a basic, simple knowledge-lean disambiguation algorithm, hereby represented by the Naïve Bayes model, can perform quite well when provided knowledge in an appropriate way. Our main conclusion will be that ...
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    22. Fextor: A Feature Extraction Framework for Natural Language Processing: A Case Study in Word Sense Disambiguation, Relation Recognition and Anaphora Resolution

      Feature extraction from text corpora is an important step in Natural Language Processing (NLP), especially for Machine Learning (ML) techniques. Various NLP tasks have many common steps, e.g. low level act of reading a corpus and obtaining text windows from it. Some high-level processing steps might also be shared, e.g. testing for morpho-syntactic constraints between words. An integrated feature extraction framework removes wasteful redundancy and helps in rapid prototyping. In this paper we present a flexible feature extraction framework called Fextor. We describe assumptions about the feature extraction process and provide general overview of software architecture. This is ...
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    25-48 of 354 « 1 2 3 4 5 ... 13 14 15 »
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