Abstract
In this work we address the problem of creating semantic term associations (key words) from a text database. The proposed method uses a hierarchical neural architecture based on the Fuzzy Adaptive Resonance Theory (ART) model. It exploits the specific statistical structure of index terms to extract semantically meaningful term associations; these are asymmetric and one-to-many due to the polysemy phenomenon. The underlying algorithm is computationally appropriate for deployment on large databases. The operation of the system is illustrated with a real database.
Financial support from DGICYT grant PB94-0374 (Spain) is gratefully appreciated. Thanks are due to J. Muruzábal and L. Tenorio for some useful comments.
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© 1996 Springer-Verlag Berlin Heidelberg
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Muñoz, A. (1996). Creating term associations using a hierarchical ART architecture. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_32
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DOI: https://doi.org/10.1007/3-540-61510-5_32
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