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<Paper uid="W04-0847">
  <Title>Optimizing Feature Set for Chinese Word Sense Disambiguation</Title>
  <Section position="10" start_page="0" end_page="0" type="concl">
    <SectionTitle>
8 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we described the implementation of I2R ! WSD system that participated in one senseval3 task: Chinese lexical sample task. An optimal feature set was selected by maximizing the cross validated accuracy of supervised Naive Bayes classifier on sense-tagged data. The senses of occurrences of target words in test data were determined using Naive Bayes classifier with optimal feature set learned from training data. Our system achieved 60.40% precision and recall in Chinese lexical sample task.</Paragraph>
  </Section>
class="xml-element"></Paper>