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

Authors:

Kristina Toutanova,

Christopher D. Manning,

Yoram SingerAuthors Info & Claims

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

Pages 173 - 180

https://doi.org/10.3115/1073445.1073478

Published: 27 May 2003 Publication History

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.

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

May 2003

293 pages

Program Chairs:
Marti Hearst,
Mari Ostendorf

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 27 May 2003

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Overall Acceptance Rate 21 of 29 submissions, 72%

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