PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation

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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.

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Tratz, Stephen C.; Sanfilippo, Antonio P.; Gregory, Michelle L.; Chappell, Alan R.; Posse, Christian & Whitney, Paul D. June 23, 2007.

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Description

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.

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  • SemEval 2007, Proceedings of the 4th International Workshop on Semantic Evaluations, June 23-24, 2007, Prague, Czech Republic, 264-267

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  • Report No.: PNNL-SA-54895
  • Grant Number: AC05-76RL01830
  • Office of Scientific & Technical Information Report Number: 924370
  • Archival Resource Key: ark:/67531/metadc898978

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  • June 23, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 30, 2016, 4:52 p.m.

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Tratz, Stephen C.; Sanfilippo, Antonio P.; Gregory, Michelle L.; Chappell, Alan R.; Posse, Christian & Whitney, Paul D. PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation, article, June 23, 2007; (digital.library.unt.edu/ark:/67531/metadc898978/: accessed October 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.