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Dynamic Composition of Information Retrieval Techniques

  • Andrew Arnt
  • Shlomo Zilberstein
  • James Allan
  • Abdel-Illah Mouaddib
Article

Abstract

This paper presents a new approach to information retrieval (IR) based on run-time selection of the best set of techniques to respond to a given query. A technique is selected based on its projected effectiveness with respect to the specific query, the load on the system, and a time-dependent utility function. The paper examines two fundamental questions: (1) can the selection of the best IR techniques be performed at run-time with minimal computational overhead? and (2) is it possible to construct a reliable probabilistic model of the performance of an IR technique that is conditioned on the characteristics of the query? We show that both of these questions can be answered positively. These results suggest a new system design that carries a great potential to improve the quality of service of future IR systems.

progressive processing information retrieval opportunity cost meta-level control 

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References

  1. Allan, J. (1995). Relevance FeedbackWith Too Much Data. In Research and Development in Information Retrieval (pp. 337-343).Google Scholar
  2. Allan, J. and Raghavan, H. (2002). Using Part-of-Speech Patterns to Reduce Query Ambiguity. In Proceedings of the 25th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval.Google Scholar
  3. Attar, R. and Fraenkel, A.S. (1977). Local Feedback in Full-Text Retrieval Systems. Journal of the ACM, 24(3), 397-417.Google Scholar
  4. Barto, A., Bradtke, S.J., and Singh, S.P. (1995). Learning to Act Using Real-Time Dynamic Programming. Artificial Intelligence, 72, 81-138.Google Scholar
  5. Belew, R.K. (1986). Adaptive Information Retrieval: Machine Learing in Associative Networks. Ph.D. thesis, University of Michigan.Google Scholar
  6. Boddy, M. and Dean, T. (1994). Decision-Theoretic Deliberation Scheduling for Problem Solving in Time-Constrained Environments. Artificial Intelligence, 67, 245-285.Google Scholar
  7. Buckley, C. and Voorhees, E.M. (2000). Evaluating Evaluation Measure Stability. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 33-40).Google Scholar
  8. Callan, J.P., Croft, W.B., and Broglio, J. (1995). TREC and Tipster Experiments with InQuery. Information Processing and Management, 31(3), 327-343.Google Scholar
  9. Chen, H. (1995). Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms. Journal of the American Society for Information Science, 46(3), 194-216.Google Scholar
  10. Conrad, J.G. and Utt, M.H. (1994). A System for Discovering Relationships by Feature Extraction from Text Databases. In W.B. Croft and C.J. van Rijsbergen (Eds.), Proceedings of the 17th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval (pp. 260-270).Google Scholar
  11. Croft,W.B., Cook, R., andWilder,D. (1995). Providing Government Information on the Internet: Experiences with THOMAS. In Proceedings of the Second Annual Conference on the Theory and Practice of Digital Libraries.Google Scholar
  12. Croft, W.B. and Harper, D.J. (1979). Using Probabilistic Models of Document Retrieval Without Relevance Information. Journal of Documentation, 35, 285-295.Google Scholar
  13. Crouch, C.J., Crouch, D.B., and Chen, Q. (2001). Initial Experiments in Short Query Retrieval. Technical Report TR-00-01, University of Minnesota Duluth.Google Scholar
  14. Davis, M. and Dunning, T. (1996). A TREC Evaluation of Query Translation Methods for Multi-Lingual Text Retrieval. In Proceedings of TREC-4.Google Scholar
  15. Dean, T. and Boddy,M. (1988). An Analysis of Time-Dependent Planning. In Proceeedings of the Seventh National Conference on Artificial Intelligence (pp. 49-54).Google Scholar
  16. Dean, T., Kaelbling, L.P., Kirman, J., and Nicholson, A. (1995). Planning Under Time Constraints in Stochastic Domains. Artificial Intelligence, 76, 35-74.Google Scholar
  17. Fahlman, S.E. (1988). An Empirical Study of Learning Speed in Back-Propagation Networks. Technical Report, Carnegie Mellon University.Google Scholar
  18. Garvey, A. and Lesser, V. (1993). Design-to-Time Real-Time Scheduling. IEEE Transactions on Systems, Man, and Cybernetics, 23(6), 1491-1502.Google Scholar
  19. Goodman, P.H. (1998). NevProp Software, Version 4. University of Nevada, Reno.Google Scholar
  20. Greenwald, L. and Dean, T. (1998). A Conditional Scheduling Approach to Designing Real-Time Systems. In Artificial Intelligence Planning Systems (pp. 224-231).Google Scholar
  21. Hansen, E.A. and Zilberstein, S. (1996). Monitoring the Progress of Anytime Problem-Solving. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 1229-1234).Google Scholar
  22. Horvitz, E.J. (1987). Reasoning About Beliefs and Actions Under Computational Resource Constraints. In Proceedings of the Workshop on Uncertainty in Artificial Intelligence.Google Scholar
  23. Horvitz, E.J. (1988). Reasoning Under Varying and Uncertain Resource Constraints. In National Conference on Artificial Intelligence (pp. 111-116).Google Scholar
  24. Horvitz, E.J. (1990). Computation and Action Under Bounded Resources. Ph.D. thesis, Stanford University.Google Scholar
  25. Horvitz, E.J. (1997). Models of Continual Computation. In Fourteenth National Conference on Artificial Intelligence (pp. 286-293).Google Scholar
  26. Leuski, A. (2001). Interactive Information Organization: Techniques and Evaluation. Ph.D. thesis, University of Massachusetts at Amherst.Google Scholar
  27. Liu, J., Lin, K., Shih,W., Yu, A., Chung, J., and Zao,W. (1991). Algortihms for Scheduling Imprecise Computations. IEEE Transactions on Computers, 24(5), 58-68.Google Scholar
  28. Mitchell, T.M. (1996). Machine Learning. New York, US: McGraw Hill.Google Scholar
  29. Mouaddib, A.I. (1993). Contribution au Raisonnement Progressif et Temps rel dans un Univers Multi-Agents. Ph.D. thesis, Univeristy of Nancy I.Google Scholar
  30. Mouaddib, A.I. and Zilberstein, S. (1997). Handling Duration Uncertainty in Meta-Level Control of Progressive Processing. In Fifteenth International Joint Conference on Artificial Intelligence (pp. 1201-1206).Google Scholar
  31. Mouaddib, A.I. and Zilberstein, S. (1998). Optimal Scheduling of Dynamic Progressive Processing. In Thirteenth Biennial European Conference on Artificial Intelligence (pp. 449-503).Google Scholar
  32. Robertson, S.E. and Walker, S. (1997). On Relevance Weights with Little Relevance Information. In Proceedings of the 20th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval (pp. 16-23).Google Scholar
  33. Russell, S. and Wefald, E. (1991). Do the Right Thing: Studies in Limited Rationality. Cambridge, MA: MIT Press.Google Scholar
  34. Singhal, A. and Pereira, F. (1999). Document Expansion for Speech Retrieval. In Research and Development in Information Retrieval (pp. 34-41).Google Scholar
  35. Sparck Jones, K. (1971). Automatic Keyword Classification for Information Retrieval. Butterworths, London.Google Scholar
  36. Sparck Jones, K. (1974). Automatic Indexing. Journal of Documentation, 30, 393-432.Google Scholar
  37. Swanson, D.R. (1988). Historical Note: Information Retrieval and the Future of an Illusion. Journal of the American Society for Information Science, 39, 92-98.Google Scholar
  38. Voorhees, E.M. (1999). Overview of the Eighth Text REtrieval Conference. In Proceedings of TREC-8.Google Scholar
  39. Xu, J. (1997). Solving theWord Mismatch Problem through Automatic Text Analysis. Ph.D. thesis, University of Massachusetts at Amherst.Google Scholar
  40. Xu, J. and Croft, W.B. (1996). Query Expansion Using Local and Global Document Analysis. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 4-11).Google Scholar
  41. Xu, J. and Croft,W.B. (2000). Improving the Effectiveness of Information Retrieval with Local Context Analysis. ACM Transactions on Information Systems, 18(1), 79-112.Google Scholar
  42. Yang, Y., Carbonell, J.G., Brown, R.D., and Frederking, R.E. (1998). Translingual Information Retrieval: Learning from Bilingual Corpora. Artificial Intelligence, 103(1/2), 323-345.Google Scholar
  43. Zilberstein, S. and Russell, S.J. (1996). Optimal Composition of Real-Time Systems. Artificial Intelligence, 82(1/2), 181-213.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Andrew Arnt
    • 1
  • Shlomo Zilberstein
    • 1
  • James Allan
    • 1
  • Abdel-Illah Mouaddib
    • 2
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherstUSA
  2. 2.Département d'informatiqueUniversité de CaenCaen CedexFrance

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