A Multiclassifier based Approach for Word Sense Disambiguation using Singular Value Decomposition

Abstract

In this paper a multiclassifier based approach is presented for a word sense disambiguation (WSD) problem. A vector representation is used for training and testing cases and the Singular Value Decomposition (SVD) technique is applied to reduce the dimension of the representation. The approach we present consists in creating a set of k-NN classifiers and combining the predictions generated in order to give a final word sense prediction for each case to be classified. The combination is done by applying a Bayesian voting scheme. The approach has been applied to a database of 100 words made available by the lexical sample WSD subtask of SemEval-2007 (task 17) organizers. Each of the words was considered an independent classification problem. A methodological parameter tuning phase was applied in order to optimize parameter setting for each word. Results achieved are among the best and make the approach encouraging to apply to other WSD tasks.