A practically unsupervised learning method to identify single-snippet answers to definition questions on the web

  • Authors:
  • Ion Androutsopoulos;Dimitrios Galanis

  • Affiliations:
  • Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece

  • Venue:
  • HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
  • Year:
  • 2005

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Abstract

We present a practically unsupervised learning method to produce single-snippet answers to definition questions in question answering systems that supplement Web search engines. The method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of positive and negative definition examples, which are then used to train an SVM to separate the two classes. We show experimentally that the proposed method is viable, that it outperforms the alternative of training the system on questions and news articles from TREC, and that it helps the search engine handle definition questions significantly better.