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<Paper uid="W99-0620">
  <Title>Learning Discourse Relations with Active Data Selection</Title>
  <Section position="8" start_page="1000" end_page="1000" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> We presented a new approach for identifying discourse relations, built upon the committee-based sampling method, in which useful examples are selected for training and those not useful are discarded. Since the committee-based sampling method was originally developed for probabilistic classifiers, we extended the method for a decision tree classifier, us- null ing a statistical technique called bootstrapping. The use of the method for learning discourse relations resulted in a drastic reduction in the amount of data required and also an increased accuracy. Further, we found that the number of bootstraps has substantial effects on performance; CBS with 500 bootstraps performed better than that with 100 bootstraps</Paragraph>
  </Section>
class="xml-element"></Paper>