Probabilistic and genetic algorithms for document retrieval

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Referenz

M. Gordon: Probabilistic and genetic algorithms for document retrieval. Communications of the ACM, 31 (10) (1988), pp. 1208–1218

DOI

http://dx.doi.org/10.1145/63039.63044

Abstract

Document retrieval systems are built to provide inquirers with computerized access to relevant documents. Such systems often miss many relevant documents while falsely identifying many non-relevant documents. Here, competing document descriptions are associated with a document and altered over time by a genetic algorithm according to the queries used and relevance judgments made during retrieval.

Extended Abstract

Bibtex

@article{Gordon:1988:PGA:63039.63044,
author = {Gordon, M.},
title = {Probabilistic and Genetic Algorithms in Document Retrieval},
journal = {Commun. ACM},
issue_date = {Oct. 1988},
volume = {31},
number = {10},
month = oct,
year = {1988},
issn = {0001-0782},
pages = {1208--1218},
numpages = {11},
url = {http://doi.acm.org/10.1145/63039.63044 http://de.evo-art.org/index.php?title=Probabilistic_and_genetic_algorithms_for_document_retrieval},
doi = {10.1145/63039.63044},
acmid = {63044},
publisher = {ACM},
address = {New York, NY, USA},
} 

Used References

1 David C Blair, Indeterminacy in the subject access to documents, Information Processing and Management: an International Journal, v.22 n.3, p.229-241, 1986 http://dx.doi.org/10.1016/0306-4573(86)90055-5

2 David C. Blair , M. E. Maron, An evaluation of retrieval effectiveness for a full-text document-retrieval system, Communications of the ACM, v.28 n.3, p.289-299, March 1985 http://doi.acm.org/10.1145/3166.3197

3 Bookstein, A., and Swanson, D.R. A decision theoretic foundation for indexing. J. Amer. Soc. Inf. Sci. 26, 1 (Jan.-Feb. 1975), 45-50.

4 Abraham Bookstein , Don Kraft, Operations Research Applied to Document Indexing and Retrieval Decisions, Journal of the ACM (JACM), v.24 n.3, p.418-427, July 1977 http://doi.acm.org/10.1145/322017.322022

5 Brauen, T. Document vector modification. In The SMART Retrieval System-Experiments in Automatic Document Processing. G. Salton, ed. Prentice-Hall, Inc., Englewood Cliffs, N.J., 1975.

6 W. S. Cooper , M. E. Maron, Foundations of Probabilistic and Utility-Theoretic Indexing, Journal of the ACM (JACM), v.25 n.1, p.67-80, Jan. 1978 http://doi.acm.org/10.1145/322047.322053

7 Croft, W.B. Document representation in probabilistic models of information retrieval. J. Amer. Soc. Inf. Sci. 32, 6 (Nov. 1981), 451-457.

8 Croft, W.B., and Harper, D.J. Using probabilistic models of documentation without relevance information. J. Doc. 35, 4 (Dec. 1979), 285-295.

9 George W. Furnas, Experience with an adaptive indexing scheme, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, p.131-135, April 1985, San Francisco, California, USA http://doi.acm.org/10.1145/317456.317480

10 Gordon, Michael D. Evaluating l he effectiveness of information retrieval systems using simulated queries. J. Amer. Soc. Inf. Sci. To appear.

11 Harter, S.P. A probabilistic approach to automatic keyword indexing. Part 1: On the distribution of specialty words in a technical literature. J. Amer. Soc. Inf. Sci. 26, 4 (July-Aug. 1975), 197-206. P~rt :2: An algorithm for probabilistic indexing. J. Amer. Soc. Inf. Sci. 26.5 (Sept.-Oct. 1975), 280-289.

12 John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992 http://dl.acm.org/citation.cfm?id=129194&CFID=558819604&CFTOKEN=68186175

13 Holland, J. H. Escaping brittleness: The possibilities of generalpurpose learning algorithms applied to parallel rule-based systems. In Machine Learning Vol. 2, R.S. Michalski, J.G. Carhonell, and T.M. Mitchell, Eds. Morgan Kaufman, Los Altos, Calif., 1986.

14 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 http://doi.acm.org/10.1145/321033.321035

15 Robertson, S.E., and Sparck Jones, K. Relevance weighting of search terms. J. Amer. Soc. Inf. Sci. 27, 3 (May-June 1976), 129-146.

16 Robertson, S.E., Maron, M.E., and Cooper, W.S. Probability of relevance: a unification of two competing models for document relrieval. Inf. Tech.: Res. Dev. 1, 1 (Jan. 1982), 1-21.

17 Salton, G., Yang, C.S., and Yu, C.T. A theory of term importance in automatic text analysis. }. Amer. Soc. Inf. Sc~ 26, 1 (1975), 33-44.

18 J Tague , C McClellan , M Nelson, The hyperterm model of a bibliographic database, Canadian Journal of Information Science, 9, p.37-58, June 1986 http://dl.acm.org/citation.cfm?id=16642&CFID=558819604&CFTOKEN=68186175

19 C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979

20 Van Rijsbergen, C.J. A theoretical basis for the use of co-occurrence data in information retrieval. J. Doc. 33, 2 (1977), 106-119. http://dl.acm.org/citation.cfm?id=539927&CFID=558819604&CFTOKEN=68186175

21 Zunde, P., and Dexter, M.E. Indexing consistency and quality. Amer. Doc. 20, 3 (July 1969}, 259-267.

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