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

• French
  • French TreeBank (FTB, Abeillé et al; 2003) Le Monde, December 2007 version, 28-tag tagset (CC tagset, Crabbé and Candito, 2008). Classical data split (10-10-80):
    • Training data: sentences 2471 to 12351
    • Development test data: sentences 1236 to 2470
    • Testing data: sentences 1 to 1235

Tables of results

WSJ

System name	Short description	Main publication	Software	Extra Data?***	All tokens	Unknown words	License
TnT*	Hidden markov model	Brants (2000)	TnT	No	96.46%	85.86%	Academic/research use only (license)
MElt	MEMM with external lexical information	Denis and Sagot (2009)	Alpage linguistic workbench	No	96.96%	91.29%	CeCILL-C
GENiA Tagger**	Maximum entropy cyclic dependency network	Tsuruoka, et al (2005)	GENiA	No	97.05%	Not available	Gratis for non-commercial usage
Averaged Perceptron	Averaged Perception discriminative sequence model	Collins (2002)	Not available	No	97.11%	Not available	Unknown
Maxent easiest-first	Maximum entropy bidirectional easiest-first inference	Tsuruoka and Tsujii (2005)	Easiest-first	No	97.15%	Not available	Unknown
SVMTool	SVM-based tagger and tagger generator	Giménez and Márquez (2004)	SVMTool	No	97.16%	89.01%	LGPL 2.1
LAPOS	Perceptron based training with lookahead	Tsuruoka, Miyao, and Kazama (2011)	LAPOS	No	97.22%	Not available	MIT
Morče/COMPOST	Averaged Perceptron	Spoustová et al. (2009)	COMPOST	No	97.23%	Not available	Non-free (academic-only)
Morče/COMPOST	Averaged Perceptron	Spoustová et al. (2009)	COMPOST	Yes	97.44%	Not available	Unknown
Stanford Tagger 1.0	Maximum entropy cyclic dependency network	Toutanova et al. (2003)	Stanford Tagger	No	97.24%	89.04%	GPL v2+
Stanford Tagger 2.0	Maximum entropy cyclic dependency network	Manning (2011)	Stanford Tagger	No	97.29%	89.70%	GPL v2+
Stanford Tagger 2.0	Maximum entropy cyclic dependency network	Manning (2011)	Stanford Tagger	Yes	97.32%	90.79%	GPL v2+
LTAG-spinal	Bidirectional perceptron learning	Shen et al. (2007)	LTAG-spinal	No	97.33%	Not available	Unknown
SCCN	Semi-supervised condensed nearest neighbor	Søgaard (2011)	SCCN	Yes	97.50%	Not available	Unknown
CharWNN	MLP with Neural Character Embeddings	dos Santos and Zadrozny (2014)	Not available	No	97.32%	89.86%	Unknown
structReg	CRFs with structure regularization	Sun(2014)	Not available	No	97.36%	Not available	Unknown
BI-LSTM-CRF	Bidirectional LSTM-CRF Model	Huang et al. (2015)	Not available	No	97.55%	Not available	Unknown
NLP4J	Dynamic Feature Induction	Choi (2016)	NLP4J	Yes	97.64%	92.03%	Apache 2

(*) 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.

FTB

System name	Short description	Main publication	Software	Extra Data?***	All tokens	Unknown words	License
Morfette	Perceptron with external lexical information*	Chrupała et al. (2008), Seddah et al. (2010)	Morfette	No	97.68%	90.52%	New BSD
SEM	CRF with external lexical information*	Constant et al. (2011)	SEM	No	97.7%	Not available	"GNU"(?)
MElt	MEMM with external lexical information*	Denis and Sagot (2009)	Alpage linguistic workbench	No	97.80%	91.77%	CeCILL-C

(*) External lexical information from the Lefff lexicon (Sagot 2010, Alexina project)

References

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

• Chrupała, Grzegorz, Dinu, Georgiana and van Genabith, Josef. 2008. Learning Morphology with Morfette. "LREC 2008".

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

• Constant, Matthieu, Tellier, Isabelle, Duchier, Denys, Dupont, Yoann, Sigogne, Anthony, and Billot, Sylvie. Intégrer des connaissances linguistiques dans un CRF : application à l'apprentissage d'un segmenteur-étiqueteur du français. "TALN'11"

• 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.

• Seddah, Djamé, Chrupała, Grzegorz, Çetinoglu, Özlem and Candito, Marie. 2010. Lemmatization and Lexicalized Statistical Parsing of Morphologically Rich Languages: the Case of French "SPMRL 2010 (NAACL 2010 workshop)"

• 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, Yusuke Miyao, and Jun’ichi Kazama. 2011. "Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models?" Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pp 238–246, 2011.

• 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.

• Sun, Xu. "Structure Regularization for Structured Prediction". In Neural Information Processing Systems (NIPS). 2402-2410. 2014

• Cicero dos Santos, and Bianca Zadrozny. "Learning character-level representations for part-of-speech tagging". In Proceedings of the 31st International Conference on Machine Learning, JMLR: W&CP volume 32. 2014.

• Z. H. Huang, W. Xu, and K. Yu. "Bidirectional LSTM-CRF Models for Sequence Tagging". In arXiv:1508.01991. 2015.

• Jinho D. Choi. 2016. Dynamic Feature Induction: The Last Gist to the State-of-the-Art, Proceedings of NAACL 2016.

See also

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