A neural cascade architecture for document retrieval

This paper describes a fuzzy neural approach adopted for information retrieval. After a thematic analysis of documents that produces two conceptual sets called themes and rhemes, a fuzzy representation is derived. The fuzzy representation reflects the hierarchical nature of texts and suggests the use of type-2 fuzzy sets. It is then translated into a cascade of two neural networks. The first level in this cascade is a fuzzy associative memory network (FAM) which maps rhemes to themes and the second level consists of a fuzzy adaptive resonance theory network (Fuzzy ART) which relates themes to document categories. The approach was experimentally evaluated.

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