PNNL: A supervised maximum entropy approach to Word Sense Disambiguation

Stephen Tratz, Antonio Sanfilippo, Michelle Gregory, Alan Chappell, Christian Posse, Paul Whitney

Research output: Contribution to conferencePaper

29 Citations (Scopus)

Abstract

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 Fscore for the fined-grained English allwords subtask of SemEval.

Original languageEnglish
Pages264-267
Number of pages4
Publication statusPublished - 1 Jan 2007
Event4th International Workshop on Semantic Evaluations, SemEval 2007 - Prague, Czech Republic
Duration: 23 Jun 200724 Jun 2007

Other

Other4th International Workshop on Semantic Evaluations, SemEval 2007
CountryCzech Republic
CityPrague
Period23/6/0724/6/07

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Cite this

Tratz, S., Sanfilippo, A., Gregory, M., Chappell, A., Posse, C., & Whitney, P. (2007). PNNL: A supervised maximum entropy approach to Word Sense Disambiguation. 264-267. Paper presented at 4th International Workshop on Semantic Evaluations, SemEval 2007, Prague, Czech Republic.