POS Tagging (State of the art)
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"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 | Jesús Giménez and Lluís Márquez. SVMTool: A general POS tagger generator based on Support Vector Machines [1] | SVMTool | 97.16% | |
| Stanford Tagger | Learning with Cyclic Dependency Network | Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network [2] | tagger | 97.24% | |
| Bidirectional Perceptron Learning | Libin Shen, Giorgio Satta and Aravind K. Joshi. Guided Learning for Bidirectional Sequence Classification [3] | POS tagger | 97.33% |