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<Paper uid="W01-1202">
  <Title>MAYA: A Fast Question-answering System Based On A Predictive Answer Indexer*</Title>
  <Section position="6" start_page="21" end_page="21" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We presented a fast and high-precision Korean QA system using a predictive answer indexer.</Paragraph>
    <Paragraph position="1"> The predictive answer indexer extracts answer candidates and terms surrounding the candidates in indexing time. Then, it stores each candidate with the surrounding terms that have specific scores in answer DB's. On the retrieval time, the QA system just calculates the similarities between a user's query and the answer candidates. Therefore, it can minimize the retrieval time and enhance the precision. Our system can easily converted into other domains because it is based on shallow NLP and IR techniques such as POS tagging, NE recognizing, pattern matching and term weighting with TF[?]IDF. The experimental results show that the QA system can improve the document retrieval precision for closed-class questions after the insignificant loss of retrieval time if it is combined with a traditional IR system. In the future, we pursue to concentrate on resolving the semantic ambiguity when a user's query matches two or more lexico-syntactic patterns.</Paragraph>
    <Paragraph position="2"> Also, we are working on an automatic and dynamic way of extending the semantic categories into which the users' queries can be more flexibly categorized.</Paragraph>
  </Section>
class="xml-element"></Paper>