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2024-03-29T00:23:19Z
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https://aclweb.org/aclwiki/index.php?title=POS_Tagging_(State_of_the_art)&diff=3849
POS Tagging (State of the art)
2007-06-20T14:21:55Z
<p>Xtag: Add (Shen, Satta and Joshi, 2007)</p>
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<div>== "Standard" measure: ==<br />
* Per token accuracy<br />
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== "Standard" datasets: ==<br />
* Training: sections 0-18 of WSJ<br />
* Testing: sections 22-24 of WSJ<br />
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{{StateOfTheArtTable}}<br />
| 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% || <br />
|-<br />
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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% ||<br />
|-<br />
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| || Bidirectional Perceptron Learning || Libin Shen, Giorgio Satta and Aravind K. Joshi. Guided 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% ||<br />
|-<br />
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|}<br />
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[[Category:State of the art]]</div>
Xtag