POS Tagging (State of the art)
"Standard" measure:
- Per token accuracy
"Standard" datasets:
- Training: sections 0-18 of WSJ
- Testing: sections 22-24 of WSJ
| System Name | Short Description | Main Publications | Software (if available) | Results | Comments (i.e. extra resources used, train/test times, ...) |
|---|---|---|---|---|---|
| SVMTool | SVM Based tagger and tagger generator | Giménez and Márquez (2004) | SVMTool | 97.16% | |
| Stanford Tagger | Learning with Cyclic Dependency Network | Toutanova et al. (2003) | Stanford Tagger | 97.24% | |
| POS tagger | Bidirectional Perceptron Learning | Shen et al. (2007) | POS tagger | 97.33% |
Giménez, J., and Márquez, L. (2004). SVMTool: A general POS tagger generator based on Support Vector Machines.
Shen, L., Satta, G., and Joshi, A. (2007). Guided learning for bidirectional sequence classification. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL 2007), pages 760-767.
Toutanova, K., Klein, D., Manning, C.D., Yoram Singer, Y. (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. Proceedings of HLT-NAACL 2003, pages 252-259.