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 language | English |
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| Pages | 264-267 |
| Number of pages | 4 |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007 - Prague, Czech Republic Duration: 23 Jun 2007 → 24 Jun 2007 |
Conference
| Conference | 4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007 |
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| Country/Territory | Czech Republic |
| City | Prague |
| Period | 23/06/07 → 24/06/07 |