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    » Automatically Discovering Word Senses
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    FLAIRS
    2001
    136views Artificial Intelligence» more  FLAIRS 2001»
    EZ.WordNet: Principles for Automatic Generation of a Coarse Grained WordNet
    13 years 9 months ago
    EZ.WordNet: Principles for Automatic Generation of a Coarse Grained WordNet
    Download  www.aaai.org
    In this paper, weproposeseveral principles that enable the automatic transformation of WordNet into a coarser grained dictionary, withoutaffecting its existing semanticrelations. ...
    Rada Mihalcea, Dan I. Moldovan
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    COLING
    1996
    113views Computational Linguistics» more  COLING 1996»
    Word Sense Disambiguation using Conceptual Density
    13 years 9 months ago
    Word Sense Disambiguation using Conceptual Density
    Download  www.lsi.upc.edu
    This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage...
    Eneko Agirre, German Rigau
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    COLING
    1990
    118views Computational Linguistics» more  COLING 1990»
    Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries
    13 years 8 months ago
    Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries
    Download  www.cs.vassar.edu
    In this paper, we describe a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonstrate the use of...
    Jean Véronis, Nancy Ide
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    ACL
    2008
    135views Computational Linguistics» more  ACL 2008»
    Choosing Sense Distinctions for WSD: Psycholinguistic Evidence
    13 years 9 months ago
    Choosing Sense Distinctions for WSD: Psycholinguistic Evidence
    Download  www.aclweb.org
    Supervised word sense disambiguation requires training corpora that have been tagged with word senses, which begs the question of which word senses to tag with. The default choice...
    Susan Windisch Brown
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    NIPS
    2008
    131views Information Technology» more  NIPS 2008»
    Unsupervised Learning of Visual Sense Models for Polysemous Words
    13 years 9 months ago
    Unsupervised Learning of Visual Sense Models for Polysemous Words
    Download  people.csail.mit.edu
    Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We...
    Kate Saenko, Trevor Darrell
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