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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Exploiting Unlabeled Data for Question Classification
verfasst von : David Tomás, Claudio Giuliano
Erschienen in: Natural Language Processing and Information Systems
Verlag: Springer Berlin Heidelberg
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In this paper, we introduce a kernel-based approach to question classification. We employed a kernel function based on latent semantic information acquired from Wikipedia. This kernel allows including external semantic knowledge into the supervised learning process. We obtained a highly effective question classifier combining this knowledge with a bag-of-words approach by means of composite kernels. As the semantic information is acquired from unlabeled text, our system can be easily adapted to different languages and domains. We tested it on a parallel corpus of English and Spanish questions.