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Leveraging Semantic Labeling for Question Matching to Facilitate Question-Answer Archive Reuse

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

A new question representation method is proposed for automated question matching over accumulated question-answer data archive. The representation defines four kinds of question words as question-type words, user-centered words, shareable-pattern words, and irrelevant words for question analysis. These question words are further annotated by a semantic labeling ontology to enhance the semantic representation for the purpose of word ambiguity reduction. We tested the matching precision on 5,000 questions with respect to various generators and the result demonstrated the stability of the method. We further compared the method with Cosine similarity and WordNet-based semantic similarity as baselines on a standard TREC dataset containing 5,536 questions. The results presented that our method improved MRR by 8.6 % and accuracy by 9.6 % on average, indicating its effectiveness.

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Notes

  1. 1.

    http://zhidao.baidu.com/.

  2. 2.

    http://wordnet.princeton.edu/.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (grant No. 61403088 and No.61305094).

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Authors and Affiliations

  1. Key Lab of Language Engineering and Computing of Guangdong Province, Guangdong University of Foreign Studies, Guangzhou, China

    Tianyong Hao

  2. Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China

    Tianyong Hao, Xinying Qiu & Shengyi Jiang

Authors
  1. Tianyong Hao
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  2. Xinying Qiu
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  3. Shengyi Jiang
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Corresponding author

Correspondence to Tianyong Hao .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Hao, T., Qiu, X., Jiang, S. (2015). Leveraging Semantic Labeling for Question Matching to Facilitate Question-Answer Archive Reuse. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

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