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) release 3 (LDC99T42). The splits of data for this task 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 publication	Software	Extra Data?***	All tokens	Unknown words
TnT*	Hidden markov model	Brants (2000)	TnT	No	96.46%	85.86%
MElt	MEMM with external lexical information	Denis and Sagot (2009)	[1]	No	96.96%	91.29%
GENiA Tagger**	Maximum entropy cyclic dependency network	Tsuruoka, et al (2005)	GENiA	No	97.05%	Not available
Averaged Perceptron	Averaged Perception discriminative sequence model	Collins (2002)	Not available	No	97.11%	Not available
Maxent easiest-first	Maximum entropy bidirectional easiest-first inference	Tsuruoka and Tsujii (2005)	Easiest-first	No	97.15%	Not available
SVMTool	SVM-based tagger and tagger generator	Giménez and Márquez (2004)	SVMTool	No	97.16%	89.01%
Morče/COMPOST	Averaged Perceptron	Spoustová et al. (2009)	[2]	No	97.23%	Not available
Stanford Tagger 1.0	Maximum entropy cyclic dependency network	Toutanova et al. (2003)	Stanford Tagger	No	97.24%	89.04%
Stanford Tagger 2.0	Maximum entropy cyclic dependency network	Manning (2011)	Stanford Tagger	No	97.29%	89.70%
Stanford Tagger 2.0	Maximum entropy cyclic dependency network	Manning (2011)	Stanford Tagger	Yes	97.32%	90.79%
LTAG-spinal	Bidirectional perceptron learning	Shen et al. (2007)	LTAG-spinal	No	97.33%	Not available
Morče/COMPOST	Averaged Perceptron	Spoustová et al. (2009)	[3]	Yes	97.44%	Not available
SCCN	Semi-supervised condensed nearest neighbor	Søgaard (2011)	SCCN	Yes	97.50%	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.

(***) Extra data: Whether system training exploited (usually large amounts of) extra unlabeled text, such as by semi-supervised learning, self-training, or using distributional similarity features, beyond the standard supervised training data.

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.

• Denis, Pascal and Sagot, Benoît. 2009. Coupling an annotated corpus and a morphosyntactic lexicon for state-of-the-art POS tagging with less human effort. "PACLIC 2009"

• 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.

• Manning, Christopher D. 2011. Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics? In Alexander Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing, 12th International Conference, CICLing 2011, Proceedings, Part I. Lecture Notes in Computer Science 6608, pp. 171--189. Springer.

• 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.

• Søgaard, Anders. 2011. Semi-supervised condensed nearest neighbor for part-of-speech tagging. The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT). Portland, Oregon.

• Spoustová, Drahomíra "Johanka", Jan Hajič, Jan Raab and Miroslav Spousta. 2009. Semi-supervised Training for the Averaged Perceptron POS Tagger. Proceedings of the 12 EACL, pages 763-771.

• 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