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<Paper uid="P02-1064">
  <Title>An Empirical Study of Active Learning with Support Vector Machines for Japanese Word Segmentation</Title>
  <Section position="10" start_page="8" end_page="8" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> To our knowledge, this is the first paper that reports the empirical results of active learning with SVMs for a more complex task in natural language processing than a text classification task. The experimental results show that SVM active learning works well for Japanese word segmentation, which is one of such complex tasks, and the naive use of a large pool with the previous method of SVM active learning is less effective. In addition, we have proposed a novel technique to improve the learning curve when using a large number of unlabeled examples and have eval- null We computed this by simple interpolation.</Paragraph>
    <Paragraph position="1">  uated it by Japanese word segmentation. Our technique outperforms the method in previous research and can significantly reduce required labeled examples to achieve a given level of accuracy.</Paragraph>
  </Section>
class="xml-element"></Paper>