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NAACL
2003

Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network

8 years 11 months ago
Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network
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.
Kristina Toutanova, Dan Klein, Christopher D. Mann
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Kristina Toutanova, Dan Klein, Christopher D. Manning, Yoram Singer
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