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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Word sense disambiguation via high order of learning in complex networks

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Autor(es):
Silva, Thiago C. [1] ; Amancio, Diego R. [2]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13560970 Sao Paulo - Brazil
[2] Univ Sao Paulo, Inst Phys Sao Carlos, BR-13560970 Sao Paulo - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: EPL; v. 98, n. 5 JUN 2012.
Citações Web of Science: 21
Resumo

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012 (AU)

Processo FAPESP: 10/00927-9 - Classificação de textos com redes complexas
Beneficiário:Diego Raphael Amancio
Linha de fomento: Bolsas no Brasil - Doutorado Direto
Processo FAPESP: 09/12329-1 - Análise de propagação de erros em aprendizado semi-supervisionado baseado em redes complexas
Beneficiário:Thiago Christiano Silva
Linha de fomento: Bolsas no Brasil - Doutorado Direto