Word sense disambiguation based on weight distribution model with multiword expression

Hee Cheol Seo, Young Sook Hwang, Hae Chang Rim

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    This paper proposes a two-phase word sense disambiguation method, which filters only the relevant senses by utilizing the multiword expression and then disambiguates the senses based on Weight Distribution Model. Multiword expression usually constrains the possible senses of a polysemous word in a context. Weight Distribution Model is based on the hypotheses that every word surrounding a polysemous word in a context contributes to disambiguating the senses according to its discrimination power. The experiments on English data in SENSEVAL-1 and SENSEVAL-2 show that multiword expression is useful to filter out irrelevant senses of a polysemous word in a given context, and Weight Distribution Model is more effective than Decision Lists.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsAlexander Gelbukh
    PublisherSpringer Verlag
    Pages176-187
    Number of pages12
    ISBN (Print)3540210067, 9783540210061
    DOIs
    Publication statusPublished - 2004

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2945
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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