Word sense disambiguation using semantic kernels with class-based term values

Abstract

In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approachesadapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluateour proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval1). With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better thanothers?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?,4.) Is their combination superior to either type?, 5.) Is ensemble of these kernels perform better than the baseline?,6.) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms canoutperform baseline in terms of F-score.