Mining the web for answers to natural language questions

  • Authors:

  • Dragomir R. Radev;Hong Qi;Zhiping Zheng;Sasha Blair-Goldensohn;Zhu Zhang;Weiguo Fan;John Prager

  • Affiliations:

  • University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;IBM TJ Watson Research Center, Hawthorne, NY

  • Venue:
  • Proceedings of the tenth international conference on Information and knowledge management
  • Year:
  • 2001

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Abstract

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. We also show how this algorithm can be combined with another algorithm (AnSel) to produce precise answers to natural language questions.