Automatic Word Sense Disambiguation (wsd) System

This paper presents an automatic word sense disambiguation (WSD) system that uses Part-of-Speech (POS) tags along with word classes as the discrete features. Word Classes are derived from the Word Class Assigner using the Word Exchange Algorithm from statistical language processing. Naïve-Bayes classifier is employed from Weka in both the training and testing phases to perform the supervised learning on the standard Senseval-3 data set. Experiments were performing using 10-fold cross-validation on the training set and the training and testing data for training the model and evaluating it. In both experiments, the features will either used separately or combined together to produce the accuracies. Results indicate that word class features did not provide any discrimination for word sense disambiguation. POS tag features produced a small improvement over the baseline. The combination of both word class and POS tag features did not increase the accuracy results. Overall, further study is likely needed to possibly improve the implementation of the word class features in the system.