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Cited by 2,844

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

Open access
Toutanova, Kristina, Klein, Dan, Manning, Christopher D., Singer, Yoram·2003·Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03

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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Metadata

Authors
Toutanova, K., Klein, D., Manning, C. D., & Singer, Y.
Venue
Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03
Published
2003
Volume
1
Pages
173-180
OpenAlex
W1996430422
Cited by
2,844
References
1
FWCI
12.52
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Feature-rich part-of-speech tagging with a cyclic dependency network. https://app.cochat.ai/papers/10.3115/1073445.1073478
The paper
Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03. https://doi.org/10.3115/1073445.1073478

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.