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

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== "Standard" measure: ==
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* Per token accuracy
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== "Standard" datasets: ==
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* Training: sections 0-18 of WSJ
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* Testing: sections 22-24 of WSJ
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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/|http://www.lsi.upc.es/~nlp/SVMTool/ || 97.16% Accuracy, Trained on WSJ 0-18, Tested on WSJ 22-24 ||  
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| 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/|http://www.lsi.upc.es/~nlp/SVMTool/ || 97.16% ||
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| --- || 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] || No || 97.24% ||
 
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Revision as of 09:56, 16 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 Jesús Giménez and Lluís Márquez. SVMTool: A general POS tagger generator based on Support Vector Machines[1] http://www.lsi.upc.es/~nlp/SVMTool/ 97.16%
--- 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 [2] No 97.24%