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    Question classification using support vector machines

    Zhang, Dell and Lee, W.S. (2003) Question classification using support vector machines. In: Clarke, C.L.A. and Cormack, G.V. and Callan, J. and Hawking, D. and Smeaton, A.F. (eds.) SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp. 26-32. ISBN 9781581136463.

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    Abstract

    Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with five machine learning algorithms: Nearest Neighbors (NN), Naive Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of-ngrams. The experiment results show that with only surface text features the SVM outperforms the other four methods for this task. Further, we propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. We describe how the tree kernel can be computed efficiently by dynamic programming. The performance of our approach is promising, when tested on the questions from the TREC QA track.

    Metadata

    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 15 Nov 2021 15:47
    Last Modified: 09 Aug 2023 12:52
    URI: https://eprints.bbk.ac.uk/id/eprint/46734

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