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<Paper uid="P98-2124">
  <Title>Word Clustering and Disambiguation Based on Co-occurrence Data</Title>
  <Section position="10" start_page="754" end_page="754" type="concl">
    <SectionTitle>
9 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have proposed a new method of clustering words based on co-occurrence data. Our method employs a probability model which naturally represents co-occurrence patterns over word pairs, and makes use of an efficient estimation algorithm based on the MDL principle. Our clustering method improves upon the previous methods proposed by Brown et al and (Li and Abe, 1996), and furthermore it can be used to derive a disambiguation method with overall disambiguation accuracy of 85.2%, which improves the performance of a state-of-the-art disambiguation method.</Paragraph>
    <Paragraph position="1"> The proposed algorithm, 2D-Clustering, can be used in practice, as long as the data size is at the level of the current Penn Tree Bank. Yet it is still relatively computationally demanding, and thus an important future task is to further improve on its computational efficiency.</Paragraph>
  </Section>
class="xml-element"></Paper>