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Proceedings Paper

Neural nets in information retrieval: a case study of the 1987 Pravda
Author(s): Jan C. Scholtes
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Paper Abstract

This paper presents an implemented neural method for free-text information filtering. A specific interest (or `query') is taught to a Kohonen feature map. By using this network as a neural filter on a dynamic free-text data base, only associated subjects are selected from this data base. The method is compared with some classical statistical information-retrieval algorithms. Various simulations show that the neural net indeed converges toward a proper representation of the query. The algorithm seems well scalable (linear complexity in time and space) resulting in high speeds, little memory needs, and easy maintainability. By combining research results from connectionist natural language processing (NLP) and information retrieval (IR), a better understanding of neural nets in NLP, a clearer view of the relation between neural nets and statistical pattern recognition, and an increased information retrieval quality are obtained.

Paper Details

Date Published: 1 July 1992
PDF: 11 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140146
Show Author Affiliations
Jan C. Scholtes, Univ. of Amsterdam (Netherlands)


Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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