Some inconsistencies and misnomers in probabilistic information retrieval

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Some inconsistencies and misnomers in probabilistic information retrieval

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval table of contents

Chicago, Illinois, United States

Pages: 57 - 61

Year of Publication: 1991

ISBN:0-89791-448-1

Author

William S. Cooper

School of Library and Information Studies, University of California, Berkeley, California

Sponsors

AICA : Assoc Italianai de Calcolo Automatico
BCS-IRSG : BCS/Information Retrieval Specialist Group
German Comp Soc : GI - Gesellshaft for Informatik
SIGIR: ACM Special Interest Group on Information Retrieval
INRIA : Institut Natl de Recherche en Info et en Automatique

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 26, Citation Count: 20

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references cited by index terms collaborative colleagues

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Cooper, W. S. Exploiting the maximum entropy principle to increase retrieval effectiveness. Journal of the American Sociely for information Science, 34(1): 31-39; 1983.
	2	Cooper, W. S.; Huizinga, P. The maximum entropy principle and its application to the design of probabilistic retrieval systems. Information Technology: Research and Development, 1(2): 99-112; 1982.
	3	Eels, E. Rational Decisions and Causality. Cambridge University Press. 1982.
	4	N. Fuhr , C. Buckley, Probabilistic document indexing from relevance feedback data, Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval, p.45-61, September 05-07, 1990, Brussels, Belgium [doi>10.1145/96749.98008]
	5	Harper, D. J.; Van Rijsbergen, C. J. An evaluation of feedback in document retrieval using co-occurrence data. Journal of Documentation, 34(3): 189-216; 1978.
	6	Kantor, P. Maximum entropy and the optimal design of automated information retrieval systems. Information Technology: Research and Development, 3(2): 88-94; 1984.
	7	Lee, J. J.; Kantor, P. A study of probabilistic information retrieval systems in the case of inconsistent expert judgement. Journal of the American Society for Information Science, 42(3), 1990.
	8	M Maron , S Curry , P Thompson, An inductive search system: Theory, design, and implementation, IEEE Transactions on Systems, Man and Cybernetics, v.16 n.1, p.21-28, Jan./Feb. 1986 [doi>10.1109/TSMC.1986.289278]
	9	M. E. Maron , J. L. Kuhns, On Relevance, Probabilistic Indexing and Information Retrieval, Journal of the ACM (JACM), v.7 n.3, p.216-244, July 1960 [doi>10.1145/321033.321035]
	10	Miller, W. H. A probabilistic search strategy for Medlars. Journal of Documentation, 27: 254-266; 1971.
	11	Robertson, S. E. Specificity and weighted retrieval. Journal of Documentation, 30: 41-46; 1974.
	12	Robertson, S. E.; Bovey, J. D. Statistical problems in the application of probabilistic models to information retrieval. British Library Research and Development Department, Report No. 5739; 1982.
	13	Robertson, S. E.; Maron, M. E.; Cooper, W. S. Probability of relevance: A unification of two competing models for document retrieval. Information Technology: Research and Development, 1(1): 1- 21; 1982.
	14	Robertson, S. E.; Sparck Jones, K. Relevance weighting of search terms.Journal of the American Society for Information Science, 27(3): 129-146; 1976.
	15	C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
	16	Yu, C.T.; Buckley, C.; Lam, H.; Salton, G. A generalized term dependence model in information retrieval. Information Technology" Research and Development, 2: 129-154; 1983.
	17	C. T. Yu , G. Salton, Precision Weighting—An Effective Automatic Indexing Method, Journal of the ACM (JACM), v.23 n.1, p.76-88, Jan. 1976 [doi>10.1145/321921.321930]

CITED BY 20

	Leah S. Larkey, Automatic essay grading using text categorization techniques, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.90-95, August 24-28, 1998, Melbourne, Australia
	Leah S. Larkey , W. Bruce Croft, Combining classifiers in text categorization, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.289-297, August 18-22, 1996, Zurich, Switzerland
	William S. Cooper , Fredric C. Gey , Daniel P. Dabney, Probabilistic retrieval based on staged logistic regression, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.198-210, June 21-24, 1992, Copenhagen, Denmark
	Warren R. Greiff , Jay M. Ponte, The maximum entropy approach and probabilistic IR models, ACM Transactions on Information Systems (TOIS), v.18 n.3, p.246-287, July 2000
	Norbert Fuhr, Integration of probabilistic fact and text retrieval, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.211-222, June 21-24, 1992, Copenhagen, Denmark
	S. E. Robertson , S. Walker, Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, p.232-241, July 03-06, 1994, Dublin, Ireland
	David D. Lewis , William A. Gale, A sequential algorithm for training text classifiers, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, p.3-12, July 03-06, 1994, Dublin, Ireland
	Thorsten Joachims, A statistical learning learning model of text classification for support vector machines, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.128-136, September 2001, New Orleans, Louisiana, United States
	Fredric C. Gey, Inferring probability of relevance using the method of logistic regression, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, p.222-231, July 03-06, 1994, Dublin, Ireland
	Bong-Hyun Cho , Changki Lee , Gary Geunbae Lee, Exploring term dependences in probabilistic information retrieval model, Information Processing and Management: an International Journal, v.39 n.4, p.505-519, July 2003
	Javed A. Aslam , Mark Montague, Models for metasearch, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.276-284, September 2001, New Orleans, Louisiana, United States
	K. L. Kwok, A network approach to probabilistic information retrieval, ACM Transactions on Information Systems (TOIS), v.13 n.3, p.324-353, July 1995
	Jianfeng Gao , Jian-Yun Nie , Guangyuan Wu , Guihong Cao, Dependence language model for information retrieval, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom
	Changki Lee , Gary Geunbae Lee, Probabilistic information retrieval model for a dependency structured indexing system, Information Processing and Management: an International Journal, v.41 n.2, p.161-175, March 2005
	Annie Y. S. Lau , Enrico W. Coiera, A Bayesian model that predicts the impact of Web searching on decision making, Journal of the American Society for Information Science and Technology, v.57 n.7, p.873-880, May 2006
	Changki Lee , Gary Geunbae Lee, Information gain and divergence-based feature selection for machine learning-based text categorization, Information Processing and Management: an International Journal, v.42 n.1, p.155-165, January 2006
	ChengXiang Zhai , John Lafferty, A risk minimization framework for information retrieval, Information Processing and Management: an International Journal, v.42 n.1, p.31-55, January 2006
	Norbert Fuhr , Ulrich Pfeifer, Probabilistic information retrieval as a combination of abstraction, inductive learning, and probabilistic assumptions, ACM Transactions on Information Systems (TOIS), v.12 n.1, p.92-115, Jan. 1994
	Donald Metzler, Generalized inverse document frequency, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA
	Giorgio Maria Di Nunzio, Using scatterplots to understand and improve probabilistic models for text categorization and retrieval, International Journal of Approximate Reasoning, v.50 n.7, p.945-956, July, 2009