Difference between revisions of "POS Tagging (State of the art)"
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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 | + | | 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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| − | | 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 | + | | 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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| − | [[Category:State | + | [[Category:State of the art]] |
Revision as of 14:55, 18 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] | SVMTool | 97.16% | |
| 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 [2] | tagger | 97.24% |