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<Paper uid="P96-1006">
  <Title>Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach</Title>
  <Section position="8" start_page="499" end_page="499" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we have presented a new approach for WSD using an exemplar based learning algorithm.</Paragraph>
    <Paragraph position="1"> This approach integrates a diverse set of knowledge sources to disambiguate word sense. When tested on a common data set, our WSD program gives higher classification accuracy than previous work on WSD.</Paragraph>
    <Paragraph position="2"> When tested on a large, separately collected data set, our program performs better than the default strategy of picking the most frequent sense. To our knowledge, this is the first time that a WSD program has been tested on such a large scale, and yielding results better than the most frequent heuristic on highly ambiguous words with the refined senses of WoRDNET.</Paragraph>
  </Section>
class="xml-element"></Paper>