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<Paper uid="W06-3005">
  <Title>A Data Driven Approach to Relevancy Recognition for Contextual Question Answering</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Contextual question answering (QA), in which users' information needs are satisfied through an interactive QA dialogue, has recently attracted more research attention. One challenge of engaging dialogue into QA systems is to determine whether a question is relevant to the previous interaction context. We refer to this task as relevancy recognition. In this paper we propose a data driven approach for the task of relevancy recognition and evaluate it on two data sets: the TREC data and the HandQA data. The results show that we achieve better performance than a previous rule-based algorithm. A detailed evaluation analysis is presented.</Paragraph>
  </Section>
class="xml-element"></Paper>