Towards faster and better retrieval models for question search

  • Authors:
  • Guangyou Zhou;Yubo Chen;Daojian Zeng;Jun Zhao

  • Affiliations:
  • Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

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Abstract

Community question answering (cQA) has become an important service due to the popularity of cQA archives on the web. This paper is concerned with the problem of question search. Question search in cQA aims to find the historical questions that are semantically equivalent or similar to the queried questions. In this paper, we propose a faster and better retrieval model for question search by leveraging user chosen category. After introducing the question category, we can filter certain amount of irrelevant historical questions under a wide range of leaf categories. Experimental results conducted on real cQA data set demonstrate that the proposed techniques are more effective and efficient than a variety of baseline methods.