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<Paper uid="H05-1121">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 963-970, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Query Expansion with the Minimum User Feedback by Transductive Learning</Title>
  <Section position="11" start_page="969" end_page="969" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we proposed a novel query expansion method which only use the minimum manual judgment. To complement the lack of relevant documents, this method utilizes the SGT transductive learning algorithm to predict the relevancy of unjudged documents. Since the performance of SGT much depends on an estimation of the fraction of relevant documents, we propose a method to sample some good fraction values. We also propose a method to laps the predictions of multiple SGT trials with above sampled fraction values and try to differentiate the importance of candidate terms for expansion in relevant documents. The experimental results showed our method outperforms other query expansion methods in the evaluations of several criteria. null</Paragraph>
  </Section>
class="xml-element"></Paper>