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Question Answering Using Enhanced Lexical Semantic Models

Wen-tau Yih, Ming-Wei Chang, Christopher Meek, and Andrzej Pastusiak


In this paper, we study the answer sentence selection problem for question answering. Unlike previous work, which primarily leverages syntactic analysis through dependency tree matching, we focus on improving the performance using models of lexical semantic resources. Experiments show that our systems can be consistently and significantly improved with rich lexical semantic information, regardless of the choice of learning algorithms. When evaluated on a benchmark dataset, the MAP and MRR scores are increased by 8 to 10 points, compared to one of our baseline systems using only surface-form matching. Moreover, our best system also outperforms pervious work that makes use of the dependency tree structure by a wide margin.


Publication typeInproceedings
Published inProceedings of the 51st Annual Meeting of the Association for Computational Linguistics
PublisherACL – Association for Computational Linguistics
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