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<Paper uid="P06-1114">
  <Title>Methods for Using Textual Entailment in Open-Domain Question Answering</Title>
  <Section position="9" start_page="911" end_page="911" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper, we discussed three different ways that a state-of-the-art textual entailment system could be used to enhance the performance of an open-domain Q/A system. We have shown that when textual entailment information is used to either lter or rank candidate answers returned by a Q/A system, Q/A accuracy can be improved from 32% to 52% (when an answer type can be detected) and from 30% to 40% (when no answer type can be detected). We believe that these results suggest that current supervised machine learning approaches to the recognition of textual entailment may provide open-domain Q/A systems with the inferential information needed to develop viable answer validation systems.</Paragraph>
  </Section>
class="xml-element"></Paper>