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Answer Quality Assessment in CQA Based on Similar Support Sets

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2015, NLP-NABD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9427))

Abstract

Community question answering portal (CQA) has become one of the most important sources for people to seek information from the Internet. With great quantity of online users ready to help, askers are willing to post questions in CQA and are likely to obtain desirable answers. However, the answer quality in CQA varies widely, from helpful answers to abusive spam. Answer quality assessment is therefore of great significance. Most of the existing approaches evaluate answer quality based on the relevance between questions and answers. Due to the lexical gap between questions and answers, these approaches are not quite satisfactory. In this paper, a novel approach is proposed to rank the candidate answers, which utilizes the support sets to reduce the impact of lexical gap between questions and answers. Firstly, similar questions are retrieved and support sets are produced with their high quality answers. Based on the assumption that high quality answers of similar questions would also have intrinsic similarity, the quality of candidate answers are then evaluated through their distance from the support sets in both aspects of content and structure. Unlike most of the existing approaches, previous knowledge from similar question-answer pairs are used to bridged the straight lexical and semantic gaps between questions and answers. Experiments are implemented on approximately 2.15 million real-world question-answer pairs from Yahoo! Answers to verify the effectiveness of our approach. The results on metrics of MAP@K and MRR show that the proposed approach can rank the candidate answers precisely.

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Notes

  1. 1.

    http://answers.yahoo.com.

  2. 2.

    http://zhidao.baidu.com.

  3. 3.

    http://www.quora.com.

  4. 4.

    http://lucene.apache.org/solr.

  5. 5.

    http://webscope.sandbox.yahoo.com.

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Acknowledgement

The work described in this paper was supported by National Natural Science Foundation of China (No. 61202362), National Key Basic Research Program of China (NO. 2013CB329606) and Project funded by China Postdoctoral Science Foundation (NO. 2013M542560).

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Authors and Affiliations

  1. School of Computer Science, National University of Defense Technology, Changsha, 410073, China

    Zongsheng Xie, Yuanping Nie, Songchang Jin, Shudong Li & Aiping Li

  2. College of Mathematics and Information Science, Shandong Institute of Business and Technology, Yantai, 264005, Shandong, China

    Shudong Li

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  1. Zongsheng Xie
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  2. Yuanping Nie
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  3. Songchang Jin
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Corresponding author

Correspondence to Zongsheng Xie .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Tsinghua University, Beijing, China

    Zhiyuan Liu

  3. Soochow University, Suzhou, Jiangsu, China

    Min Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

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Xie, Z., Nie, Y., Jin, S., Li, S., Li, A. (2015). Answer Quality Assessment in CQA Based on Similar Support Sets. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-25816-4_25

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