1. Articles in category: WSD

    25-48 of 369 « 1 2 3 4 5 ... 14 15 16 »
    1. Harmony Search Algorithm for Word Sense Disambiguation.

      Harmony Search Algorithm for Word Sense Disambiguation.

      Harmony Search Algorithm for Word Sense Disambiguation.

      PLoS One. 2015;10(9):e0136614

      Authors: Abed SA, Tiun S, Omar N

      Abstract Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s).

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      Mentions: Stanford Conrath HSA
    2. Cognitive computing The human benefit of natural language processing

      Cognitive computing The human benefit of natural language processing

      Tweet Natural language processing (NLP) is a core ability of cognitive computing systems and is often defined as helping computers process and understand human language. NLP research has been ongoing since the 1930s, and though we have made significant gains in the field, anyone who has combed through search results knows that humans have not completely bridged the communication gap with computers.

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      Mentions: Burundi Africa MIT
    3. Semantic search by means of word sense disambiguation using a lexicon

      Techniques are disclosed for analyzing a "context window" of a search query to determine a semantic meaning of a search word and to filter search results based upon the semantic meaning. Generally, a lexicon may be used to store forms, meanings, and usages of words and phrases. When a user specifies a query, a semantic analyzer obtains all of the word senses for a search word. The semantic analyzer applies lexical analysis techniques to the search word and context window to obtain a total score for each word sense and selects the word sense with the highest total score. After ...

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    4. Higher order features and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation. (arXiv:1604.02506v1 [cs.CL])

      Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word.

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    5. Word Sense Disambiguation with Neural Language Models. (arXiv:1603.07012v1 [cs.CL])

      Determining the intended sense of words in text -- word sense disambiguation (WSD) -- is a long-standing problem in natural language processing. In this paper, we present WSD algorithms which use neural network language models to achieve state-of-the-art precision. Each of these methods learns to disambiguate word senses using only a set of word senses, a few example sentences for each sense taken from a licensed lexicon, and a large unlabeled text corpus.

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    6. Ordering a lexicon network for automatic disambiguation

      Described systems and methods allow a computer system to employ a lexicon network for word sense disambiguation (WSD). In an exemplary embodiment, each node of the lexicon network represents a gloss of a lexicon entry, while an edge represents a lexical definition relationship between two glosses. The lexicon network is ordered prior to WSD, wherein ordering the lexicon network comprises arranging the nodes of the lexicon network in an ordered sequence, and removing a set of edges to eliminate loops. In some embodiments, the position of each node within the ordered sequence is determined according to a direction and a ...

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      Mentions: WSD
    7. Knowledge based word-concept model estimation and refinement for biomedical text mining.

      Knowledge based word-concept model estimation and refinement for biomedical text mining.

      Knowledge based word-concept model estimation and refinement for biomedical text mining.

      J Biomed Inform. 2015 Feb;53:300-7

      Authors: Jimeno Yepes A, Berlanga R

      Abstract Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community.

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    8. sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings. (arXiv:1511.06388v1 [cs.CL])

      Neural word representations have proven useful in Natural Language Processing (NLP) tasks due to their ability to efficiently model complex semantic and syntactic word relationships. However, most techniques model only one representation per word, despite the fact that a single word can have multiple meanings or "senses". Some techniques model words by using multiple vectors that are clustered based on context.

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    9. Polysemy in Controlled Natural Language Texts. (arXiv:1511.06591v1 [cs.CL])

      Computational semantics and logic-based controlled natural languages (CNL) do not address systematically the word sense disambiguation problem of content words, i.e., they tend to interpret only some functional words that are crucial for construction of discourse representation structures. We show that micro-ontologies and multi-word units allow integration of the rich and polysemous multi-domain background knowledge into CNL thus providing interpretation for the content words.

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      Mentions: CNL
    10. Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.

      Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.

      Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.

      Yearb Med Inform. 2015 Aug 13;10(1):194-8

      Authors: Névéol A, Zweigenbaum P

      Abstract OBJECTIVE: To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP).

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    11. A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.

      1School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, USA. 2Department of Biomedical Informatics Camridge, Vanderbilt University , Nashville, Tennessee, USA. 3Department of Library and Information Science, Yonsei University , Seoul, Korea. To save time, healthcare providers frequently use abbreviations while authoring clinical documents.

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      Mentions: Seoul Korea Texas
    12. Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain.

      Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain.

      Healthc Inform Res. 2015 Jan;21(1):35-42

      Authors: Moon S, McInnes B, Melton GB

      Abstract OBJECTIVES: Although acronyms and abbreviations in clinical text are used widely on a daily basis, relatively little research has focused upon word sense disambiguation (WSD) of acronyms and abbreviations in the healthcare domain.

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    13. A novel approach to word sense disambiguation based on topical and semantic association.

      A novel approach to word sense disambiguation based on topical and semantic association.

      A novel approach to word sense disambiguation based on topical and semantic association.

      ScientificWorldJournal. 2013;2013:586327

      Authors: Wang X, Zuo W, Wang Y

      Abstract Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context.

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      Mentions: WSD
    14. 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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    15. 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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    16. 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
    17. 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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    18. 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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    19. 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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    20. 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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    21. 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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    22. 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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    25-48 of 369 « 1 2 3 4 5 ... 14 15 16 »
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