Using Semantic Roles to Improve Question Answering
Year: 2007
Type: conference-paper
Abstract: 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.
Authors: Dan Shen, Mirella Lapata
Institutions: Saarland University, University of Edinburgh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W11155487