Advertisement

SIGIR ’94 pp 302-310 | Cite as

Passage-Level Evidence in Document Retrieval

  • James P. Callan

Abstract

The increasing lengths of documents in full-text collections encourages renewed interest in the ranking and retrieval of document passages. Past research showed that evidence from passages can improve retrieval results, but it also raised questions about how passages are defined, how they can be ranked efficiently, and what is their proper role in long, structured documents.

This paper reports on experiments with passages in INQUERY, a probabilistic information retrieval system. Experiments were conducted with passages based on paragraphs, and with passages based on text windows of various sizes. Experimental results are given for three homogeneous and two heterogeneous document collections, ranging in size from three megabytes to two gigabytes.

Keywords

Information Retrieval Average Precision Federal Register Inference Network Document Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    N. J. Belkin, C. Cool, W. B. Croft, and J. P. Callan. The effect of multiple query representations on information retrieval system performance. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Proceedings of the Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 339–346, Pittsburgh, PA, June 1993. Association for Computing Machinery.Google Scholar
  2. 2.
    C. Buckley, J., Allan, and G. Salton. Automatic routing and ad-hoc retrieval using SMART: TREC2. In D. Harman, editor, Proceedings of the Second Text REtrieval Conference (TREC-2). National Institute of Standards and Technology Special Publication 500–215, 1994.Google Scholar
  3. 3.
    J. P. Callan and W. B. Croft. An evaluation of query processing strategies using the TIPSTER collection. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Proceedings of the Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 347–356, Pittsburgh, PA, June 1993. Association for Computing Machinery.Google Scholar
  4. 4.
    J. P. Callan, W. B. Croft, and S. M. Harding. The INQUERY retrieval system. In Proceedings of the Third International Conference on Database and Expert Systems Applications,pages 78–83, Valencia, Spain, 1992. Springer-Verlag.Google Scholar
  5. 5.
    W. B. Croft, J. Callan, and J. Broglio. TREC-2 routing and ad-hoc retrieval evaluation using the INQUERY system. In D. Harman, editor, Proceedings of the Second Text REtrieval Conference (TREC-2). National Institute of Standards and Technology Special Publication 500–215, 1994.Google Scholar
  6. 6.
    Cary Griffith. WESTLAW’s winning ways. Law Office Computing, pages 31–38, February/March 1993.Google Scholar
  7. 7.
    David Haines and W. B. Croft. Relevance feedback and inference networks. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Proceedings of the Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2–11, Pittsburgh, PA, June 1993. Association for Computing Machinery.Google Scholar
  8. 8.
    D. Harman. The DARPA Tipster project. SIGIR Forum, 26 (2): 26–28, 1992.CrossRefGoogle Scholar
  9. 9.
    M. A. Hearst and C. Plaunt. Subtopic structuring for full-length document access. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Proceedings of the Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 59–68, Pittsburgh, PA, June 1993. Association for Computing Machinery.Google Scholar
  10. 10.
    A. Moffat, R. Sacks-Davis, R. Wilkinson, and J. Zobel. Retrieval of partial documents. In D. Harman, editor, Proceedings of the Second Text REtrieval Conference (TREC-2). National Institute of Standards and Technology Special Publication 500–215, 1994.Google Scholar
  11. 11.
    J. O’Connor. Answer-passage retrieval by text searching. Journal of the American Society for Information Science, 31 (4): 227–239, 1980.CrossRefGoogle Scholar
  12. 12.
    J. S. Ro. An evaluation of the applicability of ranking algorithms to improve the effectiveness of full-text retrieval. I. On the effectiveness of full-text retrieval. Journal of the American Society for Information Science, 39 (2): 73–78, 1988.CrossRefGoogle Scholar
  13. 13.
    G. Salton, J. Allan, and C. Buckley. Approaches to passage retrieval in full text information systems. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Proceedings of the Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pittsburgh, PA, June 1993. Association for Computing Machinery.Google Scholar
  14. 14.
    G. Salton and C. Buckley. Automatic text structuring and retrieval - Experiments in automatic enclopedia searching. In A. Bookstein, Y. Chiaramella, G. Salton, and V. V. Raghavan, editors, Proceedings of the Fourteenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, Chicago, IL, October 1991. Association for Computing Machinery.Google Scholar
  15. 15.
    C. Stanfill and D. L. Waltz. Statistical methods, Artificial Intelligence, and Information Retrieval. In P. S. Jacobs, editor, Text-based intelligent systems, pages 215–225. Lawrence Erlbaum, 1992.Google Scholar
  16. 16.
    H. R. Turtle and W. B. Croft. Evaluation of an inference network-based retrieval model. ACM Transactions on Information Systems, 9 (3): 187–222, 1991.CrossRefGoogle Scholar
  17. 17.
    R. Wilkinson. Effective retrieval of structured documents. In Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, July 1994. Association for Computing Machinery.Google Scholar
  18. 18.
    P. Willett. A nearest neighbor search algorithm for bibliographic retrieval from multilist files. Information Technology, 3 (2): 78–83, 1984.Google Scholar

Copyright information

© Springer-Verlag London Limited 1994

Authors and Affiliations

  • James P. Callan
    • 1
  1. 1.Computer Science DepartmentUniversity of MassachusettsAmherstUSA

Personalised recommendations