Difference between revisions of "POS Tagging (State of the art)"

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−
* '''Performance measure:''' per token accuracy
+
==Test collections==
−
* '''Training data:''' sections 0-18 of Wall Street Journal corpus
+
* '''Performance measure:''' per token accuracy. (The convention is for this to be measured on all tokens, including punctuation tokens and other unambiguous tokens.)
−
* '''Testing data:''' sections 22-24 of Wall Street Journal corpus
+
* '''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
  
−
== Table of results ==
 
  
 +
== Tables of results ==
 +
 +
===WSJ===
  
 
{| border="1" cellpadding="5" cellspacing="1" width="100%"
 
{| border="1" cellpadding="5" cellspacing="1" width="100%"
Line 11: Line 22:
 
! System name
 
! System name
 
! Short description
 
! Short description
−
! Main publications
+
! Main publication
 
! Software
 
! Software
−
! Results
+
! Extra Data?***
 +
! All tokens
 +
! Unknown words
 +
! License
 +
|-
 +
| TnT*
 +
| Hidden markov model
 +
| Brants (2000)
 +
| [http://www.coli.uni-saarland.de/~thorsten/tnt/ TnT]
 +
| No
 +
| 96.46%
 +
| 85.86%
 +
| Unknown
 +
|-
 +
| MElt
 +
| MEMM with external lexical information
 +
| Denis and Sagot (2009)
 +
| [https://gforge.inria.fr/projects/lingwb/ Alpage linguistic workbench]
 +
| No
 +
| 96.96%
 +
| 91.29%
 +
| Unknown
 +
|-
 +
| GENiA Tagger**
 +
| Maximum entropy cyclic dependency network
 +
| Tsuruoka, et al (2005)
 +
| [http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/tagger/ 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)
 +
| [http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger/ Easiest-first]
 +
| No
 +
| 97.15%
 +
| Not available
 +
| Unknown
 
|-
 
|-
 
| SVMTool
 
| SVMTool
Line 19: Line 78:
 
| Giménez and Márquez (2004)
 
| Giménez and Márquez (2004)
 
| [http://www.lsi.upc.es/~nlp/SVMTool/ SVMTool]
 
| [http://www.lsi.upc.es/~nlp/SVMTool/ SVMTool]
 +
| No
 
| 97.16%
 
| 97.16%
 +
| 89.01%
 +
| Unknown
 
|-
 
|-
−
| Stanford Tagger
+
| Morče/COMPOST
−
| learning with cyclic dependency network
+
| Averaged Perceptron
 +
| Spoustová et al. (2009)
 +
| [http://ufal.mff.cuni.cz/compost]
 +
| No
 +
| 97.23%
 +
| Not available
 +
| Unknown
 +
|-
 +
| Stanford Tagger 1.0
 +
| Maximum entropy cyclic dependency network
 
| Toutanova et al. (2003)
 
| Toutanova et al. (2003)
 
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
 
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
 +
| No
 
| 97.24%
 
| 97.24%
 +
| 89.04%
 +
| GPL v2+
 +
|-
 +
| Stanford Tagger 2.0
 +
| Maximum entropy cyclic dependency network
 +
| Manning (2011)
 +
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
 +
| No
 +
| 97.29%
 +
| 89.70%
 +
| GPL v2+
 +
|-
 +
| Stanford Tagger 2.0
 +
| Maximum entropy cyclic dependency network
 +
| Manning (2011)
 +
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
 +
| Yes
 +
| 97.32%
 +
| 90.79%
 +
| GPL v2+
 
|-
 
|-
−
| POS tagger
+
| LTAG-spinal
−
| bidirectional perceptron learning
+
| Bidirectional perceptron learning
 
| Shen et al. (2007)
 
| Shen et al. (2007)
−
| [http://www.cis.upenn.edu/~xtag/spinal/ POS tagger]
+
| [http://www.cis.upenn.edu/~xtag/spinal/ LTAG-spinal]
 +
| No
 
| 97.33%
 
| 97.33%
 +
| Not available
 +
| Unknown
 
|-
 
|-
 +
| Morče/COMPOST
 +
| Averaged Perceptron
 +
| Spoustová et al. (2009)
 +
| [http://ufal.mff.cuni.cz/compost]
 +
| Yes
 +
| 97.44%
 +
| Not available
 +
| Unknown
 +
|-
 +
| SCCN
 +
| Semi-supervised condensed nearest neighbor
 +
| Søgaard (2011)
 +
| [http://cst.dk/anders/scnn/ SCCN]
 +
| Yes
 +
| 97.50%
 +
| Not available
 +
| Unknown
 
|}
 
|}
 +
 +
(*) 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===
 +
 +
{| border="1" cellpadding="5" cellspacing="1" width="100%"
 +
|-
 +
! System name
 +
! Short description
 +
! Main publication
 +
! Software
 +
! Extra Data?***
 +
! All tokens
 +
! Unknown words
 +
|-
 +
| Morfette
 +
| Perceptron with external lexical information*
 +
| Chrupała et al. (2008), Seddah et al. (2010)
 +
| [http://sites.google.com/site/morfetteweb/ Morfette]
 +
| No
 +
| 97.68%
 +
| 90.52%
 +
|-
 +
| SEM
 +
| CRF with external lexical information*
 +
| Constant et al. (2011)
 +
| [http://www.univ-orleans.fr/lifo/Members/Isabelle.Tellier/SEM.html SEM]
 +
| No
 +
| 97.7%
 +
| Not available
 +
|-
 +
| MElt
 +
| MEMM with external lexical information*
 +
| Denis and Sagot (2009)
 +
| [https://gforge.inria.fr/projects/lingwb/ Alpage linguistic workbench]
 +
| No
 +
| 97.80%
 +
| 91.77%
 +
|}
 +
 +
(*) External lexical information from the Lefff lexicon (Sagot 2010, [https://gforge.inria.fr/frs/?group_id=482 Alexina project])
  
  
 
== References ==
 
== References ==
  
−
Giménez, J., and Márquez, L. (2004). [http://www.lsi.upc.es/~nlp/SVMTool/lrec2004-gm.pdf 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.  
+
* Brants, Thorsten. 2000. [http://acl.ldc.upenn.edu/A/A00/A00-1031.pdf TnT -- A Statistical Part-of-Speech Tagger]. "6th Applied Natural Language Processing Conference".
  
−
Shen, L., Satta, G., and Joshi, A. (2007). [http://acl.ldc.upenn.edu/P/P07/P07-1096.pdf Guided learning for bidirectional sequence classification]. ''Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL 2007)'', pages 760-767.
+
* Chrupała, Grzegorz, Dinu, Georgiana and van Genabith, Josef. 2008. [http://www.lrec-conf.org/proceedings/lrec2008/pdf/594_paper.pdf Learning Morphology with Morfette]. "LREC 2008".  
  
−
Toutanova, K., Klein, D., Manning, C.D., Yoram Singer, Y. (2003) [http://nlp.stanford.edu/kristina/papers/tagging.pdf Feature-rich part-of-speech tagging with a cyclic dependency network]. ''Proceedings of HLT-NAACL 2003'', pages 252-259.
+
* Collins, Michael. 2002. [http://people.csail.mit.edu/mcollins/papers/tagperc.pdf 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. [http://www.lirmm.fr/~lopez/TALN2011/Longs-TALN+RECITAL/Tellier_taln11_submission_54.pdf 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. [http://alpage.inria.fr/~sagot/pub/paclic09tagging.pdf 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. [http://www.lsi.upc.es/~nlp/SVMTool/lrec2004-gm.pdf 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. [http://aclweb.org/anthology-new/W/W10/W10-1410.pdf 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. [http://acl.ldc.upenn.edu/P/P07/P07-1096.pdf 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. [http://nlp.stanford.edu/kristina/papers/tagging.pdf 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. "[http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/papers/pci05.pdf 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. "[http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/papers/emnlp05bidir.pdf Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data]", ''Proceedings of HLT/EMNLP 2005'', pp. 467-474.
  
 
== See also ==
 
== See also ==
−
 
+
* [[POS Induction (State of the art)]]
 +
* [[Part-of-speech tagging]]
 
* [[State of the art]]
 
* [[State of the art]]
  
  
 
[[Category:State of the art]]
 
[[Category:State of the art]]
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Revision as of 06:12, 2 July 2012

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% Unknown
MElt MEMM with external lexical information Denis and Sagot (2009) Alpage linguistic workbench No 96.96% 91.29% Unknown
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% Unknown
Morče/COMPOST Averaged Perceptron Spoustová et al. (2009) [1] No 97.23% 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
Morče/COMPOST Averaged Perceptron Spoustová et al. (2009) [2] Yes 97.44% Not available Unknown
SCCN Semi-supervised condensed nearest neighbor Søgaard (2011) SCCN Yes 97.50% Not available Unknown

(*) 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
Morfette Perceptron with external lexical information* Chrupała et al. (2008), Seddah et al. (2010) Morfette No 97.68% 90.52%
SEM CRF with external lexical information* Constant et al. (2011) SEM No 97.7% Not available
MElt MEMM with external lexical information* Denis and Sagot (2009) Alpage linguistic workbench No 97.80% 91.77%

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


References

  • 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.
  • 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.

See also

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