POS Tagging (State of the art)
- Performance measure: per token accuracy
- Training data: sections 0-18 of Wall Street Journal corpus
- Testing data: sections 22-24 of Wall Street Journal corpus
Table of results
| System name | Short description | Main publications | Software | Results |
|---|---|---|---|---|
| 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% |
References
Giménez, J., and Márquez, L. (2004). SVMTool: A general POS tagger generator based on Support Vector Machines. Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC'04). Lisbon, Portugal.
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.
See also
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