Learning patterns to answer open domain questions on the web

Authors:
Dmitri Roussinov;Jose Robles
Affiliations:
Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ
Venue:
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2004

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Abstract

While being successful in providing keyword based access to web pages, commercial search portals still lack the ability to answer questions expressed in a natural language. We present a probabilistic approach to automated question answering on the Web, based on trainable patterns, answer triangulation and semantic filtering. In contrast to the other "shallow" approaches, our approach is entirely self-learning. It does not require any manually created scoring and filtering rules while still performing comparably. It also performs better than other fully trainable approaches.