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<Paper uid="W03-1702">
  <Title>Class Based Sense Definition Model for Word Sense Tagging and Disambiguation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present an unsupervised learning strategy for word sense disambiguation (WSD) that exploits multiple linguistic resources including a parallel corpus, a bi-lingual machine readable dictionary, and a thesaurus. The approach is based on Class Based Sense Definition Model (CBSDM) that generates the glosses and translations for a class of word senses. The model can be applied to resolve sense ambiguity for words in a parallel corpus. That sense tagging procedure, in effect, produces a semantic bilingual concordance, which can be used to train WSD systems for the two languages involved. Experimental results show that CBSDM trained on</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Longman Dictionary of Contemporary
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    </Section>
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