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<Paper uid="H05-1039">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 307-314, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Combining Deep Linguistics Analysis and Surface Pattern Learning: A Hybrid Approach to Chinese Definitional Question Answering</Title>
  <Section position="7" start_page="313" end_page="313" type="concl">
    <SectionTitle>
6 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> We have explored a hybrid approach for definitional question answering by combining deep linguistic analysis and surface pattern learning. For the first time, we have answered four questions regarding Chinese definitional QA: deep linguistic analysis and automatic pattern learning are complementary and may be combined; patterns are powerful in answering biographical questions; only a small amount of annotation (2 days) is required to obtain good performance in a biographical QA system; copulas and appositions are the most useful linguistic features; relation extraction also helps.</Paragraph>
    <Paragraph position="1"> Answering &amp;quot;What-is&amp;quot; questions is more challenging than answering &amp;quot;Who-is&amp;quot; questions. To improve the performance on &amp;quot;What-is&amp;quot; questions, we could divide &amp;quot;What-is&amp;quot; questions into finer classes such as organization, location, disease, and general substance, and process them specifically.</Paragraph>
    <Paragraph position="2"> Our current pattern matching is based on simple POS tagging which captures only limited syntactic information. We generalize words to their corresponding POS tags. Another possible improvement is to generalize using automatically derived word clusters, which provide semantic information.</Paragraph>
    <Paragraph position="3"> Acknowledgements This material is based upon work supported by the Advanced Research and Development Activity (ARDA) under Contract No. NBCHC040039. We are grateful to Linnea Micciulla for proof reading and three anonymous reviewers for suggestions on improving the paper.</Paragraph>
  </Section>
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