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• W2076987263 abstract "The growing availability of databases on the information highways motivates the development of new processing tools able to deal with a heterogeneous and changing information environment. A highly desirable feature of data processing systems handling this type of information is the ability to automatically extract its own key words. In this paper we address the specific problem of creating semantic term associations from a text database. The proposed method uses a hierarchical model made up of Fuzzy Adaptive Resonance Theory ART neural networks. First, the system uses several Fuzzy ART modules to cluster isolated words into semantic classes, starting from the database raw text. Next, this knowledge is used together with coocurrence information to extract semantically meaningful term associations. These associations are asymmetric and one-to-many due to the polisemy phenomenon. The strength of the associations between words can be measured numerically. Besides this, they implicitly define a hierarchy between descriptors. The underlying algorithm is appropriate for employment on large databases. The operation of the system is illustrated on several real databases." @default.
• W2076987263 created "2016-06-24" @default.
• W2076987263 creator A5058722811 @default.
• W2076987263 date "1997-01-01" @default.
• W2076987263 modified "2023-09-25" @default.
• W2076987263 title "Compound key word generation from document databases using a hierarchical clustering ART model" @default.
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• W2076987263 doi "https://doi.org/10.1016/s1088-467x(98)00008-0" @default.
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