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<Paper uid="W00-0104">
  <Title>Automatic Extraction of Systematic Polysemy Using Tree-cut</Title>
  <Section position="3" start_page="20" end_page="20" type="intro">
    <SectionTitle>
ARTIFACT
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    <Paragraph position="0"> airplane helicopter ball kite puzzle  at this level, where differences between the systematic relations are rather clear, and therefore lexicons that encode word senses at this level of granularity have advantages over fine-grained as well as coarse-grained lexicons in various NLP tasks.</Paragraph>
    <Paragraph position="1"> Another issue we like to address is the ways for extracting systematic polysemy. Most often, this procedure is done manually. For example, the current version of WordNet (1.6) encodes the similarity between word senses (or synsets) by a relation called cousin. But those cousin relations were identified manually by the WordNet lexicographers. A similar effort was also made in the EuroWordnet project (Vossen et al., 1999). However, manually inspecting a large, complex lexicon is very time-consuming and often prone to inconsistencies.</Paragraph>
    <Paragraph position="2"> In this paper, we propose a method which automatically extracts systematic polysemy from a hierarchically organized semantic lexicon (WordNet). Our method uses a modification of a tree generalization technique used in (Li and Abe, 1998), and generates a tree-cut, which is a list of clusters that partition a tree. Then, we compare the systematic relations extracted by our automatic method to the WordNet cousins.</Paragraph>
    <Paragraph position="3"> Preliminary results show that our method discovered most of the WordNet cousins as well as some more interesting relations.</Paragraph>
  </Section>
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