Identifying Word Senses in Greek Text: A comparison of machine learning methods

In this paper we perform a comparative evaluation of machine learning methods on the task of identifying the correct sense of a word, based on the context in which it appears. This task is known as word sense disambiguation (WSD) and is one of the hardest and most interesting issues in language engineering. Research on the use of machine learning techniques for WSD has so far focused almost exclusively on English words, due to the scarcity of the required linguistic resources for other languages. The work presented here is the first attempt to apply machine learning methods to Greek words. We have constructed a semantically tagged corpus for two Greek words: a noun with clearly distinguishable senses and a verb with overlapping senses. This corpus is used to evaluate four different machine learning methods and three different representations of the context of the ambiguous word. Our results show that the simple naïve Bayesian classifier and a method using Support Vector Machines outperform decision tree induction, even with the use of boosting. Furthermore, the use of a distance-based weighting function for the context of the ambiguous word does not seem to have a substantial effect on the performance of the methods.