Self-learning web question answering system

Authors:
Dmitri Roussinov;Jose Robles
Affiliations:
Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ
Venue:
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Year:
2004

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Abstract

While being quite successful in providing keyword based access to web pages, commercial search portals, such as Google, Yahoo, AltaVista, and AOL, still lack the ability to answer questions expressed in a natural language. In this paper, we present a probabilistic approach to automated question answering on the Web. Our approach is based on pattern matching and answer triangulation. By taking advantage of the redundancy inherent in the Web, each answer found by the system is triangulated (confirmed or disconfirmed) against other possible answers. Our approach is entirely self-learning: it does not involve any linguistic resources, nor it does require any manual tuning. Thus, the propose approach can easily be replicated in other information systems with large redundancy.