Name
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 F-score for the fined-grained English all-words subtask of SemEval. |
Year | Venue | Keywords |
|---|---|---|
2007 | SemEval@ACL | word sense disambiguation,dimension reduction,maximum entropy classifier,information gain,fined-grained english all-words subtask,highest f-score,pnnl word sense disambiguation,large number,english all-word task,supervised maximum entropy approach,supervised learning approach,rich feature,supervised learning,maximum entropy,natural language processing,computational linguistics |
DocType | Citations | PageRank |
Conference | 30 | 1.03 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Tratz | 1 | 195 | 15.29 |
| Antonio Sanfilippo | 2 | 30 | 1.03 |
| Michelle Gregory | 3 | 129 | 11.35 |
| Alan Chappell | 4 | 77 | 12.16 |
| Christian Posse | 5 | 170 | 13.23 |
| Paul Whitney | 6 | 436 | 42.33 |