Learning patterns to answer open domain questions on the web

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.

REFERENCES

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• 7: Eugene Agichtein , Steve Lawrence , Luis Gravano, Learning search engine specific query transformations for question answering, Proceedings of the 10th international conference on World Wide Web, p.169-178, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.371976]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Experimentation