Abstract
The aim of this paper is to present a new alternative to the existing Information Retrieval System (IRS) techniques, which are briefly summarized and classified. An IRS prototype has been developed with a technique based on Artificial Neural Networks which are different from those normally used for this type of applications, that is, the self-organising networks (SOM). Two types of network (radial response and multilayer perceptron) are analyzed and tested. It is concluded that, in the case of a limited number of documents and terms, the most suitable solution seems to be the Multilayer Perceptron network. The results obtained with this prototype have been positive, making the possibility of applying this technique in real size cases a cause for a certain degree of optimism.
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References
Berry, M.W; Dumais, S.T.; O.Brien, G.W. Using Linear Algebra for Intelligent Information Retrieval. Computer Science Department. University of Tennessee. (1994).
Crespo, J.L. Redes Neuronales Artificiales para Ingenieros. (1996). ISBN: 84-605-5020-6.
Croft, W.B. A model of cluster searching based on classification. Information Systems, Vol. 5, pp. 189–195.(1980).
Cutting, D.R.; Karger, D. R.; Pedersen, J. O.; Tukey, J.W. Scatter/Gather: a Cluster-based Apporach to Browsing Large Document Collections. ACM SIGIR Conference, pp 318–329. (1992).
Demuth, H.; Beale, M. Neural Network Toolboox for Use with MATLAB. User’s Guide. The Math Works Inc.
Kohonen, T. Self-Organization of Very Large Document Collections: State of Art. In Proccedings of ICNN’98, pp. 65–74.
Lagus, K. Generalizability of the WEBSOM Method to Document Collections of Various Types. European Congress Intelligent Techniques and Soft. Computing (EUFIT’98), Vol. 1, pp. 210–214.
NODELIB. Gary Willian Flakes
PDP++ Software, version 2.0. Chadley K. Dawson, Randall C. O.Reilly, J. L. McClelland
Salton, G.; McGill, M. Introduction to Modern Information Retrieval. McGraw-Hill. (1983)
Salton, G.; Yang, C.S.; Wong, A.. A Vector Space Model for Automatic Indexing. Departament of Computer Science, Cornell University. TR 14–218. (1974).
Scholtes, J.C. Neural Networks in Natural Language Processing and Information Retrieval. Ph.D. Thesis, Universiteit van Amsterdam. (1993).
SNNS, version 4.1, A. Zell et al., University of Stuttgart
Vettering, W.; Press, W.; Flannery, B.; Teukolsky, S. Numerical Recipes in C. Cambridge University Press. (1988)
Zavrel, J. An Experimental Study of Clustering and Browsing of Document Collections with Neural Networks. Ph.D. Thesis, Universiteit van Amsterdam. (1995).
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Zorrilla, M.E., Crespo, J.L., Mora, E. (2001). An Online Information Retrieval Systems by Means of Artificial Neural Networks. In: Moreno-Díaz, R., Buchberger, B., Luis Freire, J. (eds) Computer Aided Systems Theory — EUROCAST 2001. EUROCAST 2001. Lecture Notes in Computer Science, vol 2178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45654-6_27
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DOI: https://doi.org/10.1007/3-540-45654-6_27
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