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.

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@inproceedings{Toutanova2003FeatureRichPT, title={Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network}, author={Kristina Toutanova and Dan Klein and Christopher D. Manning and Yoram Singer}, booktitle={HLT-NAACL}, year={2003} }