This paper describes a set of comparative experiments, including cross{corpus evaluation, between ve alternative algorithms for supervised Word Sense Disambiguation (WSD), namely ...
Abstract. This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar–basedclassification, on the Word Sens...
In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characte...
Georgios Paliouras, Vangelis Karkaletsis, Ion Andr...
Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In ...
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-mo...