This paper describes S ENSE L EARNER – a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses. We evaluate the accuracy of S ENSE L EARNER on several standard sense-annotated data sets, and show that it compares favorably with the best results reported during the recent S ENSEVAL evaluations.