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%
GENiA Tagger	?	Tsuruoka, et al (2005)	GENiA	96.94% on WSJ, 98.26% on biomed.

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.

• Yoshimasa Tsuruoka, Yuka Tateishi, Jin-Dong Kim, Tomoko Ohta, John McNaught, Sophia Ananiadou, and Jun'ichi Tsujii, "Developing a Robust Part-of-Speech Tagger for Biomedical Text, Advances in Informatics" - 10th Panhellenic Conference on Informatics, LNCS 3746, pp. 382-392, 2005

• Yoshimasa Tsuruoka and Jun'ichi Tsujii, "Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data", Proceedings of HLT/EMNLP 2005, pp. 467-474.

See also

• Part-of-speech tagging
• State of the art