Creating term associations using a hierarchical ART architecture

  • Alberto Muñoz
Oral Presentations: Applications Scientific Applications I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)


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.

Key words

Knowledge extraction information retrieval neural ART models text databases 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Alberto Muñoz
    • 1
  1. 1.Department of Statistics and EconometricsUniversity Carlos IIIGetafe, MadridSpain

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