Exploiting unlabeled data for question classification

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Exploiting unlabeled data for question classification is a 2011 conference paper written in English by Tomas D., Giuliano C. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

[edit] Abstract

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.

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