Learning strategies for open-domain natural language question answering

Authors:
Eugene Grois;David C. Wilkins
Affiliations:
Department of Computer Science and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign;Department of Computer Science and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

We present an approach to automatically learning strategies for natural language question answering from examples composed of textual sources, questions, and answers. Our approach formulates QA as a problem of first order inference over a suitably expressive, learned representation. This framework draws on prior work in learning action and problem-solving strategies, as well as relational learning methods. We describe the design of a system implementing this model in the framework of natural language question answering for story comprehension. Finally, we compare our approach to three prior systems, and present experimental results demonstrating the efficacy of our model.