Using Semantic Roles to Improve Question Answering

Dan Shen, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Shallow semantic parsing, the automatic identification and labeling of sentential constituents, has recently received much attention. Our work examines whether semantic role information is beneficial to question answering. We introduce a general frame-work for answer extraction which exploits semantic role annotations in the FrameNet paradigm. We view semantic role assignment as an optimization problem in a bipartite graph and answer extraction as an instance of graph matching. Experimental results on the TREC datasets demonstrate improvements over state-of-the-art models.
Original languageEnglish
Title of host publicationProceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
PublisherAssociation for Computational Linguistics
Number of pages10
Publication statusPublished - 2007


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