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      Enhanced Answer Selection in CQA Using Multi-Dimensional Features Combination

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          Abstract

          Community Question Answering (CQA) in web forums, as a classic forum for user communication, provides a large number of high-quality useful answers in comparison with traditional question answering. Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects, and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multi-dimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks: (a) classifying answers as good, bad, or potentially associated with a question, and (b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.

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          Author and article information

          Journal
          TST
          Tsinghua Science and Technology
          Tsinghua University Press (Xueyan Building, Tsinghua University, Beijing 100084, China )
          1007-0214
          05 June 2019
          : 24
          : 3
          : 346-359
          Affiliations
          ∙ Hongjie Fan and Zhiyi Ma are with the School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China. E-mail: hjfan@ 123456pku.edu.cn ;
          ∙ Hongqiang Li and Dongsheng Wang are with the School of Software and Microelectronics, Peking University, Beijing 100871, China. E-mail: hongqiang.li@ 123456pku.edu.cn ; wangdsh@ 123456pku.edu.cn .
          ∙ Junfei Liu is with National Engineering Research Center for Software Engineering, Peking University, Beijing 100871, China. E-mail: liujunfei@ 123456pku.edu.cn .
          Author notes
          * To whom correspondence should be addressed. E-mail: mazhiyi@ 123456pku.edu.cn ;

          Hongjie Fan received the master degree from Peking University in 2010. He is working toward the PhD degree at Peking University. His research interests include data exchange technology, schema matching, etc.

          Zhiyi Ma is an associate professor at Peking University. His research interests include software modeling technology and model driven development. He received the PhD degree from Northeastern University, China, in 1999.

          Hongqiang Li received the bachelor degree from China University of Geo-sciences, Beijing, in 2015. He is working toward the master degree in software engineering at Peking University. His research interests include information retrieval, machine learning, etc.

          Dongsheng Wang received the bachelor degree from Northeastern University, China, in 2016. He is working toward the master degree in software engineering at Peking University. His research interests include data exchange technology, information retrieval, etc.

          Junfei Liu is a professor and PhD supervisor at School of Electronics Engineering and Computer Science, Peking University. His research interests include software engineering, information exchange technology, and smart city. He received the BS degree in 1985 from Hunan University, China, and completed the MS degree in National Defense University, China, in 1988. Then, he earned PhD degree from Peking University, China, in 1994.

          Article
          1007-0214-24-3-346
          10.26599/TST.2018.9010050

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