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<Paper uid="P02-1063">
  <Title>Revision Learning and its Application to Part-of-Speech Tagging</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we proposed the revision learning method which combines a stochastic model and a binary classifier to achieve higher performance with lower computational cost. We applied it to English POS tagging and Japanese morphological analysis, and showed improvement of accuracy with small computational cost.</Paragraph>
    <Paragraph position="1"> Compared to the conventional one-versus-rest method, revision learning has much lower computational cost with almost comparable accuracy. Furthermore, it can be applied not only to a simple multi-class classification task but also to a wider variety of problems such as Japanese morphological analysis.</Paragraph>
  </Section>
class="xml-element"></Paper>