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<Paper uid="W00-1209">
  <Title>The Research of Word Sense Disambiguation Method Based on Co-occurrence Frequency of Hownet*</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Word sense disarnbiguafion (WSD) is one of * the most difficult problems in NLP. It is helpful and in some instances required for such applications as machine translation, information retrieval, content and thematic analysis, hypertext navigation and so on. The problem of WSD was first put forward in 1949. And then in the following decades researchers adopted many methods to solve the problem of automatic word sense disambiguation, including:l) AI-based method, 2) knowledge-based method and 3) corpus-based method. 01 Although some useful results have been got, the problem of word sense disambiguation is far from being solved.</Paragraph>
    <Paragraph position="1"> The difficult of WSD is as follow: 1) Evaluation of word sense disambiguation systems is not yet standardized. 2) The potential for WSD varies by task. 3) Adequately large sense-tagged data sets are difficult to obtain. 4) The field has narrowed down approaches, but only a little. \[21 In this paper, we use a statistical based method to solve the problem of automatic word sense disambiguafion. \[31 In this method, a new knowledge base- ..... Hownet t4'5\] was use as knowledge resources. And instead of words, the sememes which are defined in Hownet were used to get the statistical figure. By doing this, the problem of data sparseness was solved to a large degree.</Paragraph>
  </Section>
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