Retrieval models for question and answer archives

  • Authors:
  • Xiaobing Xue;Jiwoon Jeon;W. Bruce Croft

  • Affiliations:
  • University of Massachusetts, Amherst, Amherst, MA, USA;Google, Inc., Mountain View, CA, USA;University of Massachusetts, Amherst, Amherst, MA, USA

  • Venue:
  • Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2008

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Abstract

Retrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, a retrieval model for this task can exploit question similarity as well as ranking the associated answers. In this paper, we propose a retrieval model that combines a translation-based language model for the question part with a query likelihood approach for the answer part. The proposed model incorporates word-to-word translation probabilities learned through exploiting different sources of information. Experiments show that the proposed translation based language model for the question part outperforms baseline methods significantly. By combining with the query likelihood language model for the answer part, substantial additional effectiveness improvements are obtained.