POS Tagging (State of the art)

Test collections

• Performance measure: per token accuracy. (The convention is for this to be measured on all tokens, including punctuation tokens and other unambiguous tokens.)
• English
  • Penn Treebank Wall Street Journal (WSJ). The splits of data for this data set were not standardized early on (unlike for parsing) and early work uses various data splits defined by counts of tokens or by sections. Most work from 2002 on adopts the following data splits, introduced by Collins (2002):
    • Training data: sections 0-18
    • Development test data: sections 19-21
    • Testing data: sections 22-24

Tables of results

WSJ

System name	Short description	Main publications	Software	All tokens	Unknown words
TnT*	Hidden markov model	Brants (2000)	TnT	96.46%	85.86%
GENiA Tagger**	Maximum entropy cyclic dependency network	Tsuruoka, et al (2005)	GENiA	97.05%	Not available
Averaged Perceptron	Averaged Perception discriminative sequence model	Collins (2002)	Not available	97.11%	Not available
Maxent easiest-first	Maximum entropy bidirectional easiest-first inference	Tsuruoka and Tsujii (2005)	Easiest-first	97.15%	Not available
SVMTool	SVM-based tagger and tagger generator	Giménez and Márquez (2004)	SVMTool	97.16%	89.01%
Stanford Tagger 1.0	Maximum entropy cyclic dependency network	Toutanova et al. (2003)	Stanford Tagger	97.24%	89.04%
Stanford Tagger 2.0	Maximum entropy cyclic dependency network	Stanford Tagger	Stanford Tagger	97.32%	90.79%
LTAG-spinal	bidirectional perceptron learning	Shen et al. (2007)	LTAG-spinal	97.33%	Not available

(*) TnT: Accuracy is as reported by Giménez and Márquez (2004) for the given test collection. Brants (2000) reports 96.7% token accuracy and 85.5% unknown word accuracy on a 10-fold cross-validation of the Penn WSJ corpus.

(**) GENiA: Results are for models trained and tested on the given corpora (to be comparable to other results). The distributed GENiA tagger is trained on a mixed training corpus and gets 96.94% on WSJ, and 98.26% on GENiA biomedical English.

References

• Brants, Thorsten. 2000. TnT -- A Statistical Part-of-Speech Tagger. "6th Applied Natural Language Processing Conference".

• Collins, Michael. 2002. Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. EMNLP 2002.

• 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.

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

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

See also

• POS Induction (State of the art)
• Part-of-speech tagging
• State of the art