High performance question/answering

  • Authors:
  • Marius A. Pasca;Sandra M. Harabagiu

  • Affiliations:
  • Southern Methodist Univ., Dallas, TX;Univ. of Texas at Austin, Austin

  • Venue:
  • Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2001

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Abstract

In this paper we present the features of a Question/Answering (Q/A) system that had unparalleled performance in the TREC-9 evaluations. We explain the accuracy of our system through the unique characteristics of its architecture: (1) usage of a wide-coverage answer type taxonomy; (2) repeated passage retrieval; (3) lexico-semantic feedback loops; (4) extraction of the answers based on machine learning techniques; and (5) answer caching. Experimental results show the effects of each feature on the overall performance of the Q/A system and lead to general conclusions about Q/A from large text collections.