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Translation Language Model Enhancement for Community Question Retrieval Using User Adoption Answer

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

Community Question Answering (CQA) services on Web provide an important alternative for knowledge acquisition. As an essential component of CQA services, question retrieval can help users save much time by finding relevant questions. However, there is a “gap” between queried questions and candidate questions, which is called lexical chasm or word mismatch problem. In this paper, we improve traditional Topic inference based Translation Language Model (T\(^2\)LM) by using the topic information of queries. Moreover, we make use of user information, specifically the number of user adoption answers, for further enhancing our proposed model. In our model, the translation model and the topic model “bridge” the word gap by linking different words. Besides, user information that has no direct relation with semantics is used to help us “bypass” the gap. By combining both of them we obtain a considerable improvement for the performance of question retrieval. Experimental results on a real Chinese CQA data set show that our proposed model improves the retrieval performance over T\(^2\)LM baseline by 7.5% in terms of Mean Average Precision (MAP).

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Notes

  1. 1.

    http://answers.yahoo.com.

  2. 2.

    http://www.zhihu.com.

  3. 3.

    http://wenwen.sogou.com.

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Acknowledgement

This research project is supported by the National Natural Science Foundation of China (Grant Nos: 61602353, 61303029), National Social Science Foundation of China (Grant No: 15BGL048), Hubei Province Science and Technology Support Project (2015BAA072), 863 Program (2015AA015403).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Ming Chen, Lin Li & Qing Xie

Authors
  1. Ming Chen
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  2. Lin Li
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  3. Qing Xie
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Corresponding author

Correspondence to Lin Li .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Chen, M., Li, L., Xie, Q. (2017). Translation Language Model Enhancement for Community Question Retrieval Using User Adoption Answer. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-63579-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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