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    IBM T. J. Watson Research Center

    Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts

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    In this paper, we introduce a WordNet- based measure of semantic relatedness by combining the structure and content of WordNet with cooccurrence information derived from raw text. We use the cooccurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the cooccurrence information.

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    Description

    Title : Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts
    Author(s) : Siddharth Patwardhan, Ted Pedersen
    Abstract : In this paper, we introduce a WordNet- based measure of semantic relatedness by combining the structure and content of WordNet with cooccurrence information derived from raw text. We use the cooccurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the cooccurrence information.
    Keywords : wsd, similarityrelatedness distributional

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2006

    Affiliations IBM T. J. Watson Research Center
    Conference_title : Proceedings of the EACL 2006 Workshop Making Sense of Sense Bringing Computational Linguistics and Psycholinguistics Together
    Pages : 1-8
    Url : http://www.d.umn.edu/~tpederse/Pubs/eacl2006-vector.pdf

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