A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering

Chen, L., Jose, J. M. , Yu, H., Yuan, F. and Zhang, D. (2016) A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering. In: Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, 22-25 Feb 2016, pp. 287-296. ISBN 9781450337168 (doi: 10.1145/2835776.2835809)

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Community Question Answering (CQA) services, such as Yahoo! Answers and WikiAnswers, have become popular with users as one of the central paradigms for satisfying users' information needs. The task of question retrieval aims to resolve one's query directly by finding the most relevant questions (together with their answers) from an archive of past questions. However, as the text of each question is short, there is usually a lexical gap between the queried question and the past questions. To alleviate this problem, we present a hybrid approach that blends several language modelling techniques for question retrieval, namely, the classic (query-likelihood) language model, the state-of-the-art translation-based language model, and our proposed semantics-based language model. The semantics of each candidate question is given by a probabilistic topic model which makes use of local and global semantic graphs for capturing the hidden interactions among entities (e.g., people, places, and concepts) in question-answer pairs. Experiments on two real-world datasets show that our approach can significantly outperform existing ones.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Chen, Dr Long and Yu, Dr Haitao
Authors: Chen, L., Jose, J. M., Yu, H., Yuan, F., and Zhang, D.
College/School:College of Science and Engineering > School of Computing Science
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
643481A Situation-aware information infrastructureDimitrios PezarosEngineering & Physical Sciences Research Council (EPSRC)EP/L026015/1COM - COMPUTING SCIENCE
651921Urban Big Data Research CentrePiyushimita ThakuriahEconomic & Social Research Council (ESRC)ES/L011921/1SPS - URBAN STUDIES