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<Paper uid="N06-1027">
  <Title>Learning to Detect Conversation Focus of Threaded Discussions</Title>
  <Section position="7" start_page="214" end_page="214" type="concl">
    <SectionTitle>
6 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> We have presented a novel feature-enriched approach for detecting conversation focus of threaded discussions for the purpose of answering student queries. Using feature-oriented link generation and a graph-based algorithm, we derived a unified framework that integrates heterogeneous sources of evidence. We explored the use of speech act analysis, lexical similarity and poster trustworthiness to analyze discussions.</Paragraph>
    <Paragraph position="1"> From the perspective of question answering, this is the first attempt to automatically answer complex and contextual discussion queries beyond factoid or definition questions. To fully automate discussion analysis, we must integrate automatic SA labeling together with our conversation focus detection approach. An automatic system will help users navigate threaded archives and researchers analyze human discussion.</Paragraph>
    <Paragraph position="2"> Supervised learning is another approach to detecting conversation focus that might be explored.</Paragraph>
    <Paragraph position="3"> The tradeoff and balance between system performance and human cost for different learning algorithms is of great interest. We are also exploring the application of graph-based algorithms to other structured-objects ranking problems in NLP so as to improve system performance while relieving human costs.</Paragraph>
  </Section>
class="xml-element"></Paper>