Long, C. and Zhang, Dell and Levene, Mark (2012) Understanding user intent in community question answering. In: Mille, A. and Gandon, F.L. and Misselis, J. and Rabinovich, M. and Staab, S. (eds.) Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion. New York, U.S.: ACM Press, pp. 823-828. ISBN 9781450312301.
Abstract
Community Question Answering (CQA) services, such as Yahoo! Answers, are specifically designed to address the innate limitation of Web search engines by helping users obtain information from a community. Understanding the user intent of questions would enable a CQA system identify similar questions, find relevant answers, and recommend potential answerers more effectively and efficiently. In this paper, we propose to classify questions into three categories according to their underlying user intent: subjective, objective, and social. In order to identify the user intent of a new question, we build a predictive model through machine learning based on both text and metadata features. Our investigation reveals that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in co-training - a semi-supervised learning framework - to make use of a large amount of unlabelled questions, in addition to the small set of manually labelled questions, for enhanced question classification. The preliminary experimental results show that co-training works significantly better than simply pooling these two types of features together.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume) |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | Administrator |
| Date Deposited: | 30 May 2013 12:16 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7094 |
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