Evaluating knowledge-poor and knowledge-rich features in automatic classification: A case study in WSD

Word Sense Disambiguation (WSD) is a fundamental task in many Computational Linguistics applications. It consists of automatically identifying the sense of ambiguous words in context using computational methods. This work evaluates the automatic disambiguation performance of five machine learning classifiers: Naive Bayes, Support Vector Machines, Decision Trees, KStar and Maximum Entropy. For the classification we compare the performance of these algorithms using knowledge-rich and knowledge-poor features applied to Portuguese data.