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    A semantic graph based topic model for question retrieval in community question answering

    Chen, Long and Jose, Joemon and Yu, Haitao and Yuan, Fajie and Zhang, Dell (2016) A semantic graph based topic model for question retrieval in community question answering. In: UNSPECIFIED (ed.) Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. New York, U.S.: ACM, pp. 287-296. ISBN 9781450337168.

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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-ofthe-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: Book Section
    Keyword(s) / Subject(s): Community Question Answering, Question Retrieval, Knowledge Repository, Topic Modelling, Language Modelling
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for
    Depositing User: Dr Dell Zhang
    Date Deposited: 11 Dec 2015 14:49
    Last Modified: 09 Aug 2023 12:37


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