We replace the overlap mechanism of the Lesk algorithm with a simple, generalpurpose Naive Bayes model that measures many-to-many association between two sets of random variables. Even with simple probability estimates such as maximum likelihood, the model gains significant improvement over the Lesk algorithm on word sense disambiguation tasks. With additional lexical knowledge from WordNet, performance is further improved to surpass the state-of-the-art results.
['Tong Wang', 'Graeme Hirst']
Applying a Naive Bayes Similarity Measure to Word Sense Disambiguation