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<Paper uid="W97-0323">
  <Title>Exemplar-Based Word Sense Disambiguation: Some Recent Improvements</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we report recent improvements to the exemplar-based learning approach for word sense disambiguation that have achieved higher disambiguation accuracy. 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 disambiguation accuracy on a large sense-tagged corpus first used in (Ng and Lee, 1996). 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 reported in (Mooney, 1996) to have the highest disambiguation accuracy among seven state-of-the-art machine learning algorithms.</Paragraph>
  </Section>
class="xml-element"></Paper>