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A Category-integrated Language Model for Question Retrieval in Community Question Answering

  • Zongcheng Ji
  • Fei Xu
  • Bin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7675)

Abstract

Community Question Answering (CQA) services have accumulated large archives of question-answer pairs, which are usually organized into a hierarchy of categories. To reuse the invaluable resources, it’s essential to develop effective Question Retrieval (QR) models to retrieve similar questions from CQA archives given a queried question. This paper studies the integration of category information of questions into the unigram Language Model (LM). Specifically, a novel Category-integrated Language Model (CLM) is proposed which views category-specific term saliency as the Dirichlet hyper-parameter that weights the parameters of LM. A point-wise divergence based measure is introduced to compute a term’s category-specific term saliency. Experiments conducted on a real world dataset from Yahoo! Answers show that the proposed CLM which integrates the category information into LM internally at the word level can significantly outperform the previous work that incorporates the category information into LM externally at the word level or at the document level.

Keywords

Community Question Answering Question Retrieval Category Category-integrated Language Model 

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References

  1. 1.
    Cai, L., Zhou, G., Liu, K., Zhao, J.: Learning the latent topics for question retrieval in community qa. In: IJCNLP, pp. 273–281 (2011)Google Scholar
  2. 2.
    Cao, X., Cong, G., Cui, B., Jensen, C.S., Zhang, C.: The use of categorization information in language models for question retrieval. In: CIKM, pp. 265–274 (2009)Google Scholar
  3. 3.
    Cao, X., Cong, G., Cui, B., Jensen, C.S.: A generalized framework of exploring category information for question retrieval in community question answer archives. In: WWW, pp. 201–210 (2010)Google Scholar
  4. 4.
    Jeon, J., Bruce Croft, W., Lee, J.H.: Finding similar questions in large question and answer archives. In: CIKM, pp. 84–90 (2005)Google Scholar
  5. 5.
    Ji, Z., Xu, F., Wang, B., He, B.: Question-answer topic model for question retrieval in community question answering. In: CIKM (2012)Google Scholar
  6. 6.
    Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: SIGIR, pp. 111–119 (2001)Google Scholar
  7. 7.
    Lee, J.-T., Kim, S.-B., Song, Y.-I., Rim, H.-C.: Bridging lexical gaps between queries and questions on large online q&a collections with compact translation models. In: EMNLP, pp. 410–418 (2008)Google Scholar
  8. 8.
    Ming, Z.-Y., Chua, T.-S., Cong, G.: Exploring domain-specific term weight in archived question search. In: CIKM, pp. 1605–1608 (2010)Google Scholar
  9. 9.
    Wang, K., Ming, Z., Chua, T.-S.: A syntactic tree matching approach to finding similar questions in community-based qa services. In: SIGIR, pp. 187–194 (2009)Google Scholar
  10. 10.
    Xue, X., Jeon, J., Bruce Croft, W.: Retrieval models for question and answer archives. In: SIGIR, pp. 475–482 (2008)Google Scholar
  11. 11.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: SIGIR, pp. 334–342 (2001)Google Scholar
  12. 12.
    Zhou, G., Cai, L., Zhao, J., Liu, K.: Phrase-based translation model for question retrieval in community question answer archives. In: ACL-HLT, pp. 653–662 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zongcheng Ji
    • 1
    • 2
  • Fei Xu
    • 1
    • 2
  • Bin Wang
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina

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