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Probabilistic question answering on the Web

Author

Listed:
  • Dragomir Radev
  • Weiguo Fan
  • Hong Qi
  • Harris Wu
  • Amardeep Grewal

Abstract

Web‐based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 on the TREC8 corpus. Our techniques have been implemented as a Web‐accessible system, called NSIR.

Suggested Citation

  • Dragomir Radev & Weiguo Fan & Hong Qi & Harris Wu & Amardeep Grewal, 2005. "Probabilistic question answering on the Web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(6), pages 571-583, April.
    • Handle: RePEc:bla:jamist:v:56:y:2005:i:6:p:571-583
    DOI: 10.1002/asi.20146
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