PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation
Stephen Tratz, Antonio P. Sanfilippo, Michelle L Gregory, Alan R. Chappell, Christian Posse, Paul Whitney · University of North Texas Digital Library (University of North Texas) · 2007
In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English all-word task in SemEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. The rich feature set combined with a Maximum Entropy classifier produces results that are significantly better than baseline and are the highest F-score for the fined-grained English allwords subtask of SemEval. 1