QuASM: a system for question answering using semi-structured data

ABSTRACT

This paper describes a system for question answering using semi-structured metadata, QuASM (pronounced "chasm"). Question answering systems aim to improve search performance by providing users with specific answers, rather than having users scan retrieved documents for these answers. Our goal is to answer factual questions by exploiting the structure inherent in documents found on the World Wide Web (WWW). Based on this structure, documents are indexed into smaller units and associated with metadata. Transforming table cells into smaller units associated with metadata is an important part of this task. In addition, we report on work to improve question classification using language models. The domain used to develop this system is documents retrieved from a crawl of www.fedstats.gov.

REFERENCES

• 1: Javed A. Aslam , Mark Montague, Bayes optimal metasearch: a probabilistic model for combining the results of multiple retrieval systems (poster session), Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.379-381, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345665]
• 2: Daniel M. Bikel , Scott Miller , Richard Schwartz , Ralph Weischedel, Nymble: a high-performance learning name-finder, Proceedings of the fifth conference on Applied natural language processing, p.194-201, March 31-April 03, 1997, Washington, DC [doi>10.3115/974557.974586]
• 3: Callan, J.P., Croft, W.B., and Harding, S.M. The INQUERY retrieval system. In Proceedings of the 3rd International Conference on Database and Expert Systems Applications (1992), pages 78--83
• 4: Finn, A., Kushmerick, N. & Smyth, B. Fact or fiction: Content classification for digital libraries. Joint DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries (Dublin), 2001
• 5: Xiaoyan Li , W. Bruce Croft, Evaluating question-answering techniques in Chinese, Proceedings of the first international conference on Human language technology research, p.1-6, March 18-21, 2001, San Diego [doi>10.3115/1072133.1072173]
• 6: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 7: Pallavi Pyreddy , W. Bruce Croft, TINTIN: a system for retrieval in text tables, Proceedings of the second ACM international conference on Digital libraries, p.193-200, July 23-26, 1997, Philadelphia, Pennsylvania, United States [doi>10.1145/263690.263816]
• 8: Saari, D. and Valgones, F. Geometry, Voting and Paradoxes. In Mathematics Magazine, pages 243--259, October, 1998
• 9: Fei Song , W. Bruce Croft, A general language model for information retrieval (poster abstract), Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.279-280, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312698]
• 10: Voorhees, E. The Trec-8 Question Answering Track Report. In Proceedings of TREC-8, 1999

INDEX TERMS

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

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

General Terms:
Algorithms, Design, Experimentation, Measurement, Performance

Keywords:
content documents, language model, metadata, question answering, question classification, semi-structured, tables