POS Tagging (State of the art)
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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
- 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):
- 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
- 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):
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 | Maximum entropy Markov model 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 perceptron | 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 | CRF with structure regularization | Sun (2014) | Not available | No | 97.36% | Not available | Unknown |
| BI-LSTM-CRF | Bidirectional LSTM-CRF | 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 |
| Flair | Bidirectional LSTM-CRF with contextual string embeddings | Akbik et al. (2018) | Flair | Yes | 97.85% | Not available | MIT |
(*) 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
- Akbik, Alan, Blythe, Duncan and Vollgraf, Roland. 2018. Contextual string embeddings for sequence labeling. COLING 2018.
- 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.