title: A topic clustering approach to finding similar questions from large question and answer archives.
abstract: With the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. Over time, a large number of question and answer (Q&A) pairs with high quality devoted by human intelligence have been accumulated as a comprehensive knowledge base. Unlike the search engines, which return long lists of results, searching in the CQA services can obtain the correct answers to the question queries by automatically finding similar questions that have already been answered by other users. Hence, it greatly improves the efficiency of the online information retrieval. However, given a question query, finding the similar and well-answered questions is a non-trivial task. The main challenge is the word mismatch between question query (query) and candidate question for retrieval (question). To investigate this problem, in this study, we capture the word semantic similarity between query and question by introducing the topic modeling approach. We then propose an unsupervised machine-learning approach to finding similar questions on CQA Q&A archives. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods.
authors.affiliation: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, Heilongjiang, China.
authors.forename: Wei-Nan
authors.initials: WN
authors.lastname: Zhang
authors.affiliation: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, Heilongjiang, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Liu
authors.affiliation: School of Computing, National University of Singapore, Singapore, Singapore.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen City, Fujian, China.
authors.forename: Liujuan
authors.initials: L
authors.lastname: Cao
authors.affiliation: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, Heilongjiang, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Cognitive Science, Xiamen University, Xiamen City, Fujian, China.
authors.forename: Rongrong
authors.initials: R
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D001109:Archives; D016000:Cluster Analysis; D019992:Databases as Topic; D016247:Information Storage and Retrieval; D008962:Models, Theoretical; D057386:Search Engine