Abstract
Natural Language Processing (NLP) for Information Retrieval has always been an interesting and challenging research area. Despite the high expectations, most of the results indicate that successfully using NLP is very complex. In this paper, we show how Support Vector Machines along with kernel functions can effectively represent syntax and semantics. Our experiments on question/answer classification show that the abovemodels highly improve on bag-of-words on a TREC dataset. © 2008 Association for Computational Linguistics.
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CITATION STYLE
Moschitti, A., & Quarteroni, S. (2008). Kernels on linguistic structures for answer extraction. In ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 113–116). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1557690.1557720
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