A comparative study of support vector machines applied to the supervised word sense disambiguation problem in the medical domain

We have applied five supervised learning approaches to word sense disambiguation in the medical domain. Our objective is to evaluate Support Vector Machines (SVMs) in comparison with other well known supervised learning algorithms including the naive Bayes classifier, C4.5 decision trees, decision lists and boosting approaches. Based on these results we introduce further refinements of these approaches. We have made use of unigrarn and bigram features selected using different fre quency cut-off values and window sizes along with the statistical signif icance test of the log likelihood measure for bigrams. Our results show that overall, the best SVM model was most accurate in 27 of 60 cases, compared to 22, 14, 10 and 14 for the naive Bayes, C4.5 decision trees, decision list and boosting methods respectively.