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<Paper uid="C92-2070">
  <Title>Walker, Donald (1987), &amp;quot;Ka\]owledge Resource Tools for Aeo~'ssing</Title>
  <Section position="9" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7. Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper has described an approach to word sense disambiguation using statistical models of word classes.</Paragraph>
    <Paragraph position="1"> This method overcomes the knowledge acquisition bottleneck faced by word-specific sense discriminators.</Paragraph>
    <Paragraph position="2"> By entirely circumventing the issue of polysemy AClXS DE COLING-92, NANqVS, 23-28 Aovr 1992 4 5 9 PRec. OF COLING-92, NAI'~fES, AUG. 23-28. 1992 resolution in training material acquisition, the system has acquired an extensive set of sense discriminators from unrestricted monolingnal texts withoat haman intervention. Class models also offer the additional advantages of smaller model storage requirements and increased implementation efficiency due to reduced dimensionality. Also, they can correctly identify a word sense which occurs rarely or only once in the corpus -performance unattainable by statistically trained word-specific models. These advances are not without cost, as class-based models have diluted discriminating power and may not capture highly indicative collocations specific to only one word. Despite the inherent handicaps, the system performs better than several previous approaches, based on a direct comparison of results for the same words.</Paragraph>
  </Section>
class="xml-element"></Paper>