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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Word sense disambiguation via high order of learning in complex networks

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Silva, Thiago C. [1] ; Amancio, Diego R. [2]
Total Authors: 2
[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
Total Affiliations: 2
Document type: Journal article
Source: EPL; v. 98, n. 5 JUN 2012.
Web of Science Citations: 21

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)

FAPESP's process: 10/00927-9 - Using complex networks to classify texts
Grantee:Diego Raphael Amancio
Support type: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 09/12329-1 - Error propagation analysis in complex network based on semi-supervised learning
Grantee:Thiago Christiano Silva
Support type: Scholarships in Brazil - Doctorate (Direct)