Feature-rich part-of-speech tagging with a cyclic dependency network

  • Authors:

  • Kristina Toutanova;Dan Klein;Christopher D. Manning;Yoram Singer

  • Affiliations:

  • Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;The Hebrew University, Jerusalem, Israel

  • Venue:
  • NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
  • Year:
  • 2003

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Abstract

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result.