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<Paper uid="P97-1030">
  <Title>Mistake-Driven Mixture of Hierarchical Tag Context Trees</Title>
  <Section position="10" start_page="235" end_page="235" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have described a new tag model that uses mistake-driven mixture to produce hierarchical tag context trees that can deal with exceptional connections whose detection is not possible at part-of-speech level. Our experinaental results show that combining hierarchical tag context trees with the mistake-driven mixture method is extremely effective for 1. incorporating exceptional connections and 2. avoiding data over-fitting. Although we have focused on part-of-speech tagging in this paper, the mistake-driven mixture method should be useful for other applications because detecting and incorporating exceptions is a central problem in corpus-based NLP. We are now costructing a Japanese dependency parser that employes mistake-driven mixture of decision trees.</Paragraph>
  </Section>
class="xml-element"></Paper>