A new fuzzy rule-based classification system for word sense disambiguation

Authors:
S. M. Fakhrahmad;A. R. Rezapour;M. Zolghadri Jahromi;M. H. Sadreddini
Affiliations:
Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Venue:
Intelligent Data Analysis
Year:
2012

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Abstract

Word sense disambiguation WSD can be thought of as the most challenging task in the process of machine translation. Various supervised and unsupervised learning methods have already been proposed for this purpose. In this paper, we propose a new efficient fuzzy classification system in order to be applied for WSD. In order to optimize the generalization accuracy, we use rule-weight as a simple mechanism to tune the classifier and propose a new learning method to iteratively adjust the weight of fuzzy rules. Through computer simulations on TWA data as a standard corpus, the proposed scheme shows a uniformly good behavior and achieves results which are comparable or better than other classification systems, proposed in the past.