COMPARATIVE ANALYSIS OF DIFFERENT CLASSIFIERS FOR CASE BASED MODEL IN PUNJABI WORD SENSE DISAMBIGUATION

Abstract

Research is being carried out for machines to be able to better decipher an ambiguous word. The majority of work done in Punjabi, a regional language of India and one among the 10 most spoken languages of the world, is limited to knowledge- based techniques. The implementation of Case Based Model to help decipher the Punjabi ambiguous word is new and hence the results determined can be beneficial exemplar in Punjabi Word Sense Disambiguation research. Vectorization of the sentence is done to use minimal features to help find the right context of the given ambiguous word. Four different measuring functions are used to measure the nearness of the given sample with respect to store sample, thereby using the concept of case- based reasoning. The collected sample is then subjected to four different classifiers, namely Naïve Bayes, k-Nearest Neighbor, Decision Tree and Artificial Neural Network to find the closest context. The experimentation shows the variation in results subject to the size of the vector.