Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1230–1238, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities Baoxun Wang, Xiaolong Wang, Chengjie Sun, Bingquan Liu, Lin Sun School of Computer Science and Technology Harbin Institute of Technology Harbin, China {bxwang, wangxl, cjsun, liubq, lsun}@insun.hit.edu.cn Abstract Quantifying the semantic relevance be- tween questions and their candidate an- swers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven question- answering (cQA) dataset and the forum dataset, we present a novel learning strat- egy to promote the performance of our method on the social community datasets without hand-annotating work. The ex- perimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora. 1 Introduction In natural language processing (NLP) and infor- mation retrieval (IR) fields, question answering (QA) problem has attracted much attention over the past few years. Nevertheless, most of the QA researches mainly focus on locating the exact an- swer to a given factoid question in the related doc- uments. The most well known international evalu- ation on the factoid QA task is the Text REtrieval Conference (TREC)1, and the annotated questions and answers released by TREC have become im- portant resources for the researchers. However, when facing a non-factoid question such as why, how, or what about, however, almost no automatic QA systems work very well. The user-generated question-answer pairs are definitely of great importance to solve the non- factoid questions. Obviously, these natural QA pairs are usually created during people’s com- munication via Internet social media, among which we are interested in the community-driven 1http://trec.nist.gov question-answering (cQA) sites and online fo- rums. The cQA sites (or systems) provide plat- forms where users can either ask questions or de- liver answers, and best answers are selected man- ually (e.g., Baidu Zhidao2 and Yahoo! Answers3). Comparing with cQA sites, online forums have more virtual society characteristics, where people hold discussions in certain domains, such as tech- niques, travel, sports, etc. Online forums contain a huge number of QA pairs, and much noise infor- mation is involved. To make use of the QA pairs in cQA sites and online forums, one has to face the challenging problem of distinguishing the questions and their answers from the noise. According to our investi- gation, the data in the community based sites, es- pecially for the forums, have two obvious charac- teristics: (a) a post usually includes a very short content, and when a person is initializing or re- plying a post, an informal tone tends to be used; (b) most of the posts are useless, which makes the community become a noisy environment for question-answer detection. In this paper, a novel approach for modeling the semantic relevance for QA pairs in the social me- dia sites …

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