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Query Modulation For Web-Based Question Answering

  • Dragomir R. Radev
  • Hong Qi
  • Zhiping Zheng
  • Sasha Blair-Goldensohn
  • Zhu Zhang
  • Weiguo Fan
  • John Prager
Part of the Text, Speech and Language Technology book series (TLTB, volume 32)

The web is now becoming one of the largest information and knowledge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the “correct” answers to factual natural language questions. We propose a probabilistic algorithm called QASM (Question Answering using Statistical Models) that learns the best query paraphrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation.

Keywords

Search Engine Noun Phrase Expectation Maximization Algorithm Question Answering Statistical Machine Translation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2008

Authors and Affiliations

  • Dragomir R. Radev
    • 1
  • Hong Qi
    • 2
  • Zhiping Zheng
    • 3
  • Sasha Blair-Goldensohn
    • 4
  • Zhu Zhang
    • 5
  • Weiguo Fan
    • 6
  • John Prager
    • 7
  1. 1.University of MichiganAnn ArborUSA
  2. 2.Lu Jia Zui Finance and Trade ZoneChina
  3. 3.PortlandUSA
  4. 4.Columbia UniversityNew YorkUSA
  5. 5.The University of ArizonaTucsonUSA
  6. 6.Virginia Polytechnic Institute and State UniversityBlacksburgUSA
  7. 7.IBM T.J. Watson Research CenterYorktown HeightsUSA

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