Combining counterpropagation neural networks and defeasible logic programming for text classification

• Autores: Gómez, Sergio Alejandro; Chesñevar, Carlos Iván
• Año de publicación: 2004
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; ARTIFICIAL INTELLIGENCE; información; Neural nets; Machine Learning; Defeasible Argumentation; Counterpropagation neural networks; text mining
• Institución: Universidad Nacional de La Plata

The increasing growth of documents available in the World Wide Web has resulted in a difficult situation for those end-users who search for a particular piece of information. A common approach to facilitate search is to perform document classification first, learning the topology of a document base as a set of clusters. Clusters will be labeled as relevant or irrelevant, and determining whether a new document belongs to a given cluster can help determine whether such document corresponds to the user information needs. We contend that the above clustering technique can be enriched by additional filtering criteria specified in terms of Defeasible Logic Programming (DeLP). In this paper we discuss a combination of Counterpropagation Neural Networks for clustering and DeLP to solve the problem of classifying documents according to user-specified criteria. We present an example of how the proposed approach works.
Eje: Sistemas de información y Metaheurística
Red de Universidades con Carreras en Informática (RedUNCI)