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<Paper uid="W97-0323">
  <Title>Exemplar-Based Word Sense Disambiguation: Some Recent Improvements</Title>
  <Section position="7" start_page="211" end_page="211" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In summary, we have presented improvements to the exemplar-based learning approach for WSD. By using a larger value of k, the number of nearest neighbors to use for determining the class of a test example, and through 10-fold cross validation to automatically determine the best k, we have obtained improved disambignation accuracy on a large sense-tagged corpus. The accuracy achieved by our improved exemplar-based classifier is comparable to the accuracy on the same data set obtained by the Naive-Bayes algorithm, which was recently reported to have the highest disambignation accuracy among seven state-of-the-art machine learning algorithms.</Paragraph>
  </Section>
class="xml-element"></Paper>