Web question answering through automatically learned patterns

ABSTRACT

While being 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. We explore the feasibility of a completely trainable approach to the automated question answering on the Web or large scale digital libraries. By using the inherent redundancy of large scale collections, each candidate answer found by the system is triangulated (confirmed or disconfirmed) against other possible answers. Since our approach is entirely self-learning and does not involve any linguistic resources it can be easily implemented within digital libraries or Web search portals.

REFERENCES

• 1: 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]
• 2: Susan Dumais , Michele Banko , Eric Brill , Jimmy Lin , Andrew Ng, Web question answering: is more always better?, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564428]
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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

Keywords:
Algorithms, Experimentation