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<Paper uid="P04-1081">
  <Title>A Kernel PCA Method for Superior Word Sense Disambiguation Dekai WU1 Weifeng SU Marine CARPUAT dekai@cs.ust.hk weifeng@cs.ust.hk marine@cs.ust.hk</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> This work represents, to the best of our knowledge, the first application of Kernel PCA to a true natural language processing task. We have shown that a KPCA-based model can significantly outperform state-of-the-art results from both na&amp;quot;ive Bayes as well as maximum entropy models, for supervised word sense disambiguation. The fact that our KPCA-based model outperforms the SVM-based model indicates that kernel methods other than SVMs deserve more attention. Given the theoretical advantages of KPCA, it is our hope that this work will encourage broader recognition, and further exploration, of the potential of KPCA modeling within NLP research.</Paragraph>
    <Paragraph position="1"> Given the positive results, we plan next to combine large amounts of unsupervised data with reasonable smaller amounts of supervised data such as the Senseval lexical sample. Earlier we mentioned that one of the promising advantages of KPCA is that it computes the transform purely from unsupervised training vector data. We can thus make use of the vast amounts of cheap unannotated data to augment the model presented in this paper.</Paragraph>
  </Section>
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