Affiliations: Department of Computer Science and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Correspondence:
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Corresponding author: Abdoreza Rezapour, Department of Computer Science and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. E-mail: Abdoreza.Rezapour@gmail.com.
Abstract: . Word Sense Disambiguation, which is one of the most challenging problems in the process of machine translation, can be considered as a classification problem. In this paper, we use K-Nearest-Neighbor, as one of the most popular classification methods, as well as some knowledge based resources in order to design a WSD scheme. The success of K-Nearest-Neighbor is tightly dependent on two factors; the features used to represent the context in which an ambiguous word occurs and the distance/similarity measure used for comparison of text vectors. Hence, in the present study, we focus on these two matters. For the first purpose, we extract three sets of features; syntactic features, lexical features and semantic features. In order to produce enriched and useful corpora, we apply preprocessed steps. In this work, we carry out a feature selection process as well as a feature weighting policy in order to fine-tune the classifier. For the second purpose, we try several distance/similarity metrics (rather than one metric) in order to find the most proper one. We also assign and use feature weights and propose a weighted formula for every metric. Moreover, to show that the proposed schemes are not language-dependent, we apply the suggested schemes to two sets of data; English and Persian corpora. The evaluation results, with regards to the feature selection and feature weighting strategies, show that the semantic and syntactic features have a significant effect on the classification ability of the system. The results are also encouraging compared to state of the art.
Keywords: Machine translation, word sense disambiguation, K-Nearest-Neighbor, similarity metric, distance metric, feature weighting, feature selection
