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. (?) | Stanford Tagger | 97.24% | |
Bidirectional Perceptron Learning | Shen et al. (?) | POS tagger | 97.33% |
Jesús Giménez and Lluís Márquez. (2004). SVMTool: A general POS tagger generator based on Support Vector Machines.
Libin Shen, Giorgio Satta and Aravind K. Joshi. (?). Guided Learning for Bidirectional Sequence Classification.
Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. (?) Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network.