- 1 J. Allan. Personal Communication.Google Scholar
- 2 J. Allan. Incremental relevance feedback for information filtering. In Proceedings o/the Nineteenth Annual International A CM SIGIR Conference on Research and Development in Information Retrieval, pages 270-278. Association for Computing Machinery, New York, Aug. 1996. Google ScholarDigital Library
- 3 J. Allan, L. Bdlesteros, J. Callas, W. Croft, and Z. Lu. Recent experiments with INQUERY. In Proceedings of the Fourth Text REtrieval Conference (TREC-4), pages 49-64. NIST Special Publication 500-236, October 1996.Google Scholar
- 4 N. Belkin and W. Croft. Information filtering emd information retrieval: Two sides of the same coin? Communications of the A CM, 35(12):29-38, 1992. Google ScholarDigital Library
- 5 C. Bucldey and G. Sedton. Optimization of relevance feedback weights. In Proceedings ol the Eighteenth Annual international A CM SIGIR Conlerence on Research and Development in Inlormation Retrieval, pages 351-357. Association for Computing Machinery, New York, July 1995. Google ScholarDigital Library
- 6 C. Bucldey, G. Salton, and J. Allan. The effect of adding relevance information in a relevance feedback environment. In Proceedings o} the Seventeenth Annual International A CM SIGIR Conlerence on Research and Development in In.formation Retrieval, pages 292-300. Springer-Verlag, New York, July 1994. Google ScholarDigital Library
- 7 C. Bucldey, A. Singhal, and M. Mitre,. Using query zoning and correlation within SMART : TREC 5. In Proceedings o} the Fifth Tezt REtrieval Conlerence (TREC-5). NIST Special Publication, 1997. To appear.Google Scholar
- 8 C. Buckley, A. Singhal, M. Mitra, and G. Sultan. New retrieval approaches using SMART : TREC 4. In Proceedings of the Fourth Tezt REtrieval Conference (TREC-4), pages 25-48. NIST Special Publication 500- 236, October 1996.Google Scholar
- 9 J. Callan. Information filtering with inference networks. In Proceedings of the Nineteenth Annual International A CM SIGIR Conference on Research and Development in Information Retrieval, pages 262-269. Association for Computing Machinery, New York, Aug. 1996. Google ScholarDigital Library
- 10 D. T. Davis and J.-N. Hwang. Attentional focus training by boundary region data selection. In International Joint Conference on Neural Networks, pages 1-676 to 1-681, Baltimore, MD, June 7-I1 1992.Google Scholar
- 11 D. K. Harman. Overview of the third Text REtrieval Conference (TREC-3). in Proceedings of the Third Text REtrieval Conference (TREC-3), pages 1-19. NIST Special Publication 500-225, April 1995.Google ScholarCross Ref
- 12 D. K. Harma#a. Overview of the fourth Text RE- trieval Conference (TREC-4). In Proceedings of the Fourth Text REtrieval Conference (TREC-4), pages 1- 24. NIST Special Publication 500-236, October 1996.Google Scholar
- 13 D. K. HarmaJa. Overview of the fifth Text REtrieval Conference (TREC-5). In Proceedings oj the Filth Text REtrieval Conference (TREC-5), 1997 (to appear).Google Scholar
- 14 D. Harper. Relevance Feedback in Document Retrieval Systems. PhD thesis, University of Cambridge, England, 1980.Google Scholar
- 15 D. Hull. Improving text retrieval for the routing problem using latent semantic indexing. In Proceedings of the Seventeenth Annual International A CM SIGIR Conference on Research and Development in information Retrieval, pages 282-291. Springer-Verlag, New York, July 1994. Google ScholarDigital Library
- 16 K. Kwok and L. Grunfeld. TREC-4 ad-hoc, routing retrieval and filtering experiments using PIRCS. In Proceedings st the Fourth Text REtrieval Conference (TREC.4), pages 145-152. NIST Special Publication 500-236, October 1996.Google Scholar
- 17 K. Kwok and L. Grunfeld. TREC-5 english and chinese retrieval experiments using PIRCS. In Proceedings of the Fifth Tezt REtrieval Conference (TREC-5), 1997 (to appear).Google Scholar
- 18 D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of the Seventeenth Annual International A CA{ SIGIR Conference on Research and Development in Information Retrieval, pages 3-12. Springer-Verlag, New York, July 1994. Google ScholarDigital Library
- 19 M. Plutowski mad H. White. Selecting concise training sets from clean data. IEEE Transactions on Neural Networks, 4(2):305-318, Max. 1993.Google ScholarDigital Library
- 20 S. Robertson, S. Walker, M. Hancock-Beaulieu, M. Gutford, mad A. Payne. Okapi at TREC-4. In Proceedings of the Fourth Tezt REtrieval Conference (TREC- 4), pages 73-96. NIST Special Publication 500-236, October 1996.Google Scholar
- 21 S. Robertson, S. Walker, S. Jones, M. Hancock- Beaulieu, and M. Gatfonl. Okapi at TREC-3. In Proceedings o.f the Third Text REtrieval Conference (TREC-3), pages 109-126. NIST Special Publication 500-225, April 1995.Google Scholar
- 22 J. Rocchio. Document Retrieval Systems-Optimization and Evaluation. PhD thesis, Harvard Computational Laboratory, Caxnbridge, MA, 1966.Google Scholar
- 23 J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval Sllstem--E#7#eriments in Automatic Document Processing, pages 313-323, Englewood Cliffs, NJ, 1971. Prentice Hall, Inc.Google Scholar
- 24 G. Sultan and C. Bucidey. Term-weighting approaches in automatic text retrieval. In.formation Processing and Management, 24(5):513-523, 1988. Google ScholarDigital Library
- 25 G. Sultan and C. Bucldey. improving retrieval performance by relevance feedback. Journal oj the American Society }or Information Science, 41(4):288-297, 1990.Google Scholar
- 26 G. Salton and M. McGill. Introduction to Modern In. formation Retrieval. McGraw Hill Book Co., New York, 1983. Google ScholarDigital Library
- 27 G. Salton, A. Wong, and C. Yang. A vector space model for information retrieval. Communications of the A CM, 18(11):613-620, November 1975. Google ScholarDigital Library
- 28 H. Schutze, D. Hull, and J. Pedersen. A comparison of classifiers and document representations for the routing problem. In Proceedings of the Eighteenth Annual International A CM SIGIR Conlerence on Research and Development in lnlormation Retrieval, pages 229-237. Association for Computing Machinery, New York, July 1995. Google ScholarDigital Library
- 29 H. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifl.h Annual A CM Workshop on Computational Learning Theory, pages 287-294. ACM Press, July 1992. Google ScholarDigital Library
- 30 A. Singhal, C. Buckley, and M. Mitra. Pivoted document length normalization. In Proceedings of the Nineteenth Annual International ACM SIGIR Con.terence on Research and Development in Information Retrieval, pages 21-29. Association for Computing Machinery, New York, Aug. 1996. Google ScholarDigital Library
Index Terms
- Learning routing queries in a query zone
Recommendations
Determinacy and query rewriting for conjunctive queries and views
Answering queries using views is the problem which examines how to derive the answers to a query when we only have the answers to a set of views. Constructing rewritings is a widely studied technique to derive those answers. In this paper we consider ...
Answering Queries Using Limited External Query Processors
When answering queries using external information sources, the contents of the queries can be described by views. To answer a query, we must rewrite it using the set of views presented by the sources. When the external information sources also have the ...
View-based query processing for regular path queries with inverse
PODS '00: Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systemsView-based query processing is the problem of computing the answer to a query based on a set of materialized views, rather than on the raw data in the database. The problem comes in two different forms, called query rewriting and query answering, ...
Comments