A new fuzzy rule-based classification system for word sense disambiguation
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.
classification word sense disambiguation machine translation generalization accuracy rule-weight fuzzy systems