Combining model-oriented and description-oriented approaches for probabilistic indexing

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Combining model-oriented and description-oriented approaches for probabilistic indexing

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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: 46 - 56

Year of Publication: 1991

ISBN:0-89791-448-1

Authors

Norbert Fuhr	Technische Hochschule Darmstadt, Germany
Ulrich Pfeifer	Universität Dortmund, Lehrstuhl Informatik VI, W-4600 Dortmund 50, Germany and Technische Hochschule Darmstadt, Germany

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): 10, Downloads (12 Months): 21, Citation Count: 9

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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	Fienberg, S. (1980). The Analysis of Cross-Classified Categorial Data. MIT Press, Cambridge, Mass., 2. edition.
	2	D. H. Freeman, Jr., Applied categorical data analysis, Marcel Dekker, Inc., New York, NY, 1987
	3	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]
	4	Norbert Fuhr , Chris Buckley, A probabilistic learning approach for document indexing, ACM Transactions on Information Systems (TOIS), v.9 n.3, p.223-248, July 1991 [doi>10.1145/125187.125189]
	5	N. Fuhr , G. E. Knorz, Retrieval test evaluation of a rule based automatic indexing (AIR/PHYS), Proc. of the third joint BCS and ACM symposium on Research and development in information retrieval, p.391-408, November 1984, King's College, Cambridge
	6	Fuhr, N. (1989a). Log-Linear Indexing Functions and the Darmstadt Indexing Approach. Internal Report DV II 89-4, TH Darmstadt, FB Informatik, Datenverwaltungssysteme Ii.
	7	Norbert Fuhr, Models for retrieval with probabilistic indexing, Information Processing and Management: an International Journal, v.25 n.1, p.55-72, 1989 [doi>10.1016/0306-4573(89)90091-5]
	8	Norbert Fuhr, Optimum polynomial retrieval functions based on the probability ranking principle, ACM Transactions on Information Systems (TOIS), v.7 n.3, p.183-204, July 1989 [doi>10.1145/65943.65944]
	9	Fuhr, N.; Hartmann, S.; Knorz, G.; Lustig, G.; Schwantner, M.; Tzeras, K. (1991). AII~/X- a Rule-Based Multistage Indexing System for Large Subject Fields. In: Proceedings of the RIAO'91, Barcelona, Spain, April 2-5, 1991.
	10	Knorz, G. (1983a). A utomalisches Indexieren als Erkennen abslrakler Objekte. Niemeyer, Tiibingen.
	11	Knorz, G. (1983b). Development of Aulomalic indezing for the AIR Retrieval Test. Experiments by means of ALIBABA. Report DVII 83-3, TH Darmstadt, FB Informatik, Datenverwaltungssysteme Ii.
	12	Knorz, G. (1986). Die Anwendung von Polynomklassifikatoren fiir die automatische Indexierung. In: Lustig, G. (ed.): A ulomalische Indexierung zwischen Forschung und Anwendung, pages 98-126. Olms, Hildesheim.
	13	Pfeifer, U. (1990). Development of Log-Linear and Linear-Itera~ive Indexing Functions (in German). Diploma thesis, TH Darmstadt, FB Informatik, Datenverwaltungssysteme II.
	14	Ulrich Pfeifer, Entwicklung linear-iterativer und logistischer Indexierungsfunktionen, Proceedings of the GI/GMD-Workshop on Information Retrieval, p.38-50, June 23-24, 1991
	15	van Rijsbergen, C. (1977). A Theoretical Basis for the Use of Co-Occurrence Data in Information Retrieval. Journal of Documentation 33, pages 106-119.
	16	Robertson, S.; Bovey, J. (1982). Statistical Problems in the application of probabilistic models ~o information retrieval. Report 5739, British Library, London.
	17	Robertson, S.; Sparek Jones, K. (1976). Relevance Weighting of Search Terms. Journal of the American Society for Information Science 27, pages 129-146.
	18	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]
	19	Yu, C.; Buekley, C.; Lain, K.; Salton, G. (1983). A Generalized Term Dependence Model in Information Retrieval. Information Technology: Research and Development 2, pages 129-154.

CITED BY 9

	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
	Fabio Crestani , Mounia Lalmas , Cornelis J. Van Rijsbergen , Iain Campbell, “Is this document relevant?…probably”: a survey of probabilistic models in information retrieval, ACM Computing Surveys (CSUR), v.30 n.4, p.528-552, Dec. 1998
	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
	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
	Chidanand Apté , Fred Damerau , Sholom M. Weiss, Automated learning of decision rules for text categorization, ACM Transactions on Information Systems (TOIS), v.12 n.3, p.233-251, July 1994
	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
	Aynur Dayanik , David D. Lewis , David Madigan , Vladimir Menkov , Alexander Genkin, Constructing informative prior distributions from domain knowledge in text classification, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA
	Tong Zhang , Frank J. Oles, Text Categorization Based on Regularized Linear Classification Methods, Information Retrieval, v.4 n.1, p.5-31, April 2001
	Henrik Nottelmann , Norbert Fuhr, From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications, Information Retrieval, v.6 n.3-4, p.363-388, September-December 2003