In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English All-Word task in Se-mEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. Our Maximum Entropy approach combined with a rich set of features produced results that are significantly better than baseline and are the highest F-score for the fined-grained English All-Words subtask.
Revised: February 22, 2008 |
Published: June 23, 2007
Citation
Tratz S.C., A.P. Sanfilippo, M.L. Gregory, A.R. Chappell, C. Posse, and P.D. Whitney. 2007.PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation. In SemEval 2007, Proceedings of the 4th International Workshop
on Semantic Evaluations, June 23-24, 2007, Prague, Czech Republic, 264-267. Stroudsburg, Pennsylvania:Association for Computational Linguistics. PNNL-SA-54895.