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<Paper uid="P05-1027">
  <Title>Question Answering as Question-Biased Term Extraction: A New Approach toward Multilingual QA</Title>
  <Section position="8" start_page="220" end_page="220" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper described a novel approach to extracting answers to a question using probabilistic models constructed from only question-answer pairs.</Paragraph>
    <Paragraph position="1"> This approach requires no question type system, no named entity extractor, and no class name extractor.</Paragraph>
    <Paragraph position="2"> To the best of our knowledge, no previous study has regarded Question Answering as Question-Biased Term Extraction. As a feasibility study, we built a QA system using Maximum Entropy Models on a 2000-question/answer dataset. The results were evaluated by 10-fold cross validation, which showed that the performance is 0.36 in MRR and 0.47 in Top5. Since this approach relies on a morphological analyzer, applying the QBTE Model 1 to QA tasks of other languages is our future work.</Paragraph>
  </Section>
class="xml-element"></Paper>