Combining counterpropagation neural networks and defeasible logic programming for text classification

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...

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Detalles Bibliográficos
Autores principales: Gómez, Sergio Alejandro, Chesñevar, Carlos Iván
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2004
Materias:
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
información
Neural nets
Machine Learning
Defeasible Argumentation
Counterpropagation neural networks
text mining
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21334
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21334
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
información
Neural nets
Machine Learning
Defeasible Argumentation
Counterpropagation neural networks
text mining
spellingShingle Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
información
Neural nets
Machine Learning
Defeasible Argumentation
Counterpropagation neural networks
text mining
Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
Combining counterpropagation neural networks and defeasible logic programming for text classification
topic_facet Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
información
Neural nets
Machine Learning
Defeasible Argumentation
Counterpropagation neural networks
text mining
description 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.
format Objeto de conferencia
Objeto de conferencia
author Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
author_facet Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
author_sort Gómez, Sergio Alejandro
title Combining counterpropagation neural networks and defeasible logic programming for text classification
title_short Combining counterpropagation neural networks and defeasible logic programming for text classification
title_full Combining counterpropagation neural networks and defeasible logic programming for text classification
title_fullStr Combining counterpropagation neural networks and defeasible logic programming for text classification
title_full_unstemmed Combining counterpropagation neural networks and defeasible logic programming for text classification
title_sort combining counterpropagation neural networks and defeasible logic programming for text classification
publishDate 2004
url http://sedici.unlp.edu.ar/handle/10915/21334
work_keys_str_mv AT gomezsergioalejandro combiningcounterpropagationneuralnetworksanddefeasiblelogicprogrammingfortextclassification
AT chesnevarcarlosivan combiningcounterpropagationneuralnetworksanddefeasiblelogicprogrammingfortextclassification
bdutipo_str Repositorios
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