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<Paper uid="C04-1132">
  <Title>Learning a Robust Word Sense Disambiguation Model using Hypernyms in Definition Sentences</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper proposes a method to improve the robustness of a word sense disambiguation (WSD) system for Japanese. Two WSD classifiers are trained from a word sense-tagged corpus: one is a classifier obtained by supervised learning, the other is a classifier using hypernyms extracted from definition sentences in a dictionary. The former will be suitable for the disambiguation of high frequency words, while the latter is appropriate for low frequency words. A robust WSD system will be constructed by combining these two classifiers. In our experiments, the F-measure and applicability of our proposed method were 3.4% and 10% greater, respectively, compared with a single classifier obtained by supervised learning.</Paragraph>
  </Section>
class="xml-element"></Paper>