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<Paper uid="W04-0822">
  <Title>Augmenting Ensemble Classification for Word Sense Disambiguation with a Kernel PCA Model</Title>
  <Section position="5" start_page="0" end_page="0" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have described our word sense disambiguation system and its performance on the Senseval-3 English, Chinese, and Multilingual Lexical Sample tasks. The system consists of an ensemble classifier utilizing combinations of maximum entropy, boosting, na&amp;quot;ive Bayes, and a new Kernel PCA based model.</Paragraph>
    <Paragraph position="1"> We have demonstrated that our new model based on Kernel PCA is, along with maximum entropy, one of the most accurate stand-alone models voting in the ensemble, as evaluated under carefully controlled to ensure the same optimized feature set across all models being compared. Moreover, we have shown that the KPCA model exhibits a significantly different classification bias, a characteristic that makes it a valuable voter in an ensemble. The results confirm that accuracy is generally improved by the addition of the KPCA-based model.</Paragraph>
  </Section>
class="xml-element"></Paper>