POS Tagging (State of the art)

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  • 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) [http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/tagger/ GENiA 96.94% on WSJ, 98.26 on biomed.


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