POS Tagging (State of the art)

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"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%
Bidirectional Perceptron Learning Libin Shen, Giorgio Satta and Aravind K. Joshi. Guided Learning for Bidirectional Sequence Classification [3] POS tagger 97.33%