Dan Shen and Mirella Lapata. 2007. Using Semantic Roles to Improve Question Answering. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and on Computational Natural Language Learning, 12-21. Prague.

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 framework 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.

@InProceedings{Shen:Lapata:07,
  author = 	 {Dan Shen and Mirella Lapata},
  title = 	 {Using Semantic Roles to Improve Question Answering},
  pages =        {12--21},
  crossref =	 {EMNLP:CONLL:07}
}

@Proceedings{EMNLP:CONLL:07,
  title = 	 {Proceedings  of the Conference on Empirical Methods
                 in Natural Language Processing and on Computational
                 Natural Language  Learning}, 
  booktitle =    {Proceedings  of the Conference on Empirical Methods
                 in Natural Language Processing and on Computational
                 Natural Language  Learning}, 
  year = 	 2007,
  address =	 {Prague}
}