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<Paper uid="W04-0822">
  <Title>Augmenting Ensemble Classification for Word Sense Disambiguation with a Kernel PCA Model</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The HKUST word sense disambiguation systems benefit from a new nonlinear Kernel Principal Component Analysis (KPCA) based disambiguation technique. We discuss and analyze results from the Senseval-3 English, Chinese, and Multi-lingual Lexical Sample data sets. Among an ensemble of four different kinds of voted models, the KPCA-based model, along with the maximum entropy model, outperforms the boosting model and na&amp;quot;ive Bayes model. Interestingly, while the KPCA-based model typically achieves close or better accuracy than the maximum entropy model, nevertheless a comparison of predicted classifications shows that it has a significantly different bias. This characteristic makes it an excellent voter, as confirmed by results showing that removing the KPCA-based model from the ensemble generally degrades performance. null</Paragraph>
  </Section>
class="xml-element"></Paper>