Name
Abstract | ||
|---|---|---|
Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks. |
Year | Venue | Keywords |
|---|---|---|
2010 | ACL (System Demonstrations) | available open source wsd,different classifier,semeval task,free text,different preprocessing tool,supervised english all-words wsd,wide-coverage word sense disambiguation,supervised learning,flexible framework,best performance,semeval workshop,additional feature,knowledge base,support vector machine |
Field | DocType | Volume |
SemEval,Computer science,Support vector machine,Supervised learning,Preprocessor,Artificial intelligence,Natural language processing,Classifier (linguistics),Machine learning,Word-sense disambiguation | Conference | P10-4 |
Citations | PageRank | References |
95 | 2.56 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Zhong | 1 | 232 | 8.96 |
| Hwee Tou Ng | 2 | 4092 | 300.40 |