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

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(Add (Shen, Satta and Joshi, 2007))
("Standard" datasets:)
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{{StateOfTheArtTable}}
 
{{StateOfTheArtTable}}
| SVMTool || SVM Based tagger and tagger generator || Jesús Giménez and Lluís Márquez. SVMTool: A general POS tagger generator based on Support Vector Machines [http://www.lsi.upc.es/~nlp/SVMTool/lrec2004-gm.pdf] || [http://www.lsi.upc.es/~nlp/SVMTool/ SVMTool] || 97.16% ||  
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| SVMTool || SVM Based tagger and tagger generator || Giménez and Márquez (2004) || [http://www.lsi.upc.es/~nlp/SVMTool/ SVMTool] || 97.16% ||  
 
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| Stanford Tagger || Learning with Cyclic Dependency Network || Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network [http://nlp.stanford.edu/kristina/papers/tagging.pdf] || [http://nlp.stanford.edu/software/tagger.shtml tagger] || 97.24% ||
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| Stanford Tagger || Learning with Cyclic Dependency Network || Toutanova et al. (?) || [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger] || 97.24% ||
 
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|  || Bidirectional Perceptron Learning || Libin Shen, Giorgio Satta and Aravind K. JoshiGuided Learning for Bidirectional Sequence Classification  [http://acl.ldc.upenn.edu/P/P07/P07-1096.pdf] || [http://www.cis.upenn.edu/~xtag/spinal/ POS tagger] || 97.33% ||
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|  || Bidirectional Perceptron Learning || Shen et al. (?) || [http://www.cis.upenn.edu/~xtag/spinal/ POS tagger] || 97.33% ||
 
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Jesús Giménez and Lluís Márquez. (2004). [http://www.lsi.upc.es/~nlp/SVMTool/lrec2004-gm.pdf SVMTool: A general POS tagger generator based on Support Vector Machines].
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Libin Shen, Giorgio Satta and Aravind K. Joshi. (?). [http://acl.ldc.upenn.edu/P/P07/P07-1096.pdf Guided Learning for Bidirectional Sequence Classification].
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Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. (?) [http://nlp.stanford.edu/kristina/papers/tagging.pdf Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network].
  
 
[[Category:State of the art]]
 
[[Category:State of the art]]

Revision as of 20:22, 20 June 2007

"Standard" measure:

  • Per token accuracy

"Standard" datasets:

  • Training: sections 0-18 of WSJ
  • Testing: sections 22-24 of WSJ


System Name Short Description Main Publications Software (if available) Results Comments (i.e. extra resources used, train/test times, ...)
SVMTool SVM Based tagger and tagger generator Giménez and Márquez (2004) SVMTool 97.16%
Stanford Tagger Learning with Cyclic Dependency Network Toutanova et al. (?) Stanford Tagger 97.24%
Bidirectional Perceptron Learning Shen et al. (?) POS tagger 97.33%


Jesús Giménez and Lluís Márquez. (2004). SVMTool: A general POS tagger generator based on Support Vector Machines.

Libin Shen, Giorgio Satta and Aravind K. Joshi. (?). Guided Learning for Bidirectional Sequence Classification.

Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. (?) Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network.