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.

• Type: Conference
• Year: 2003
• Where: NAACL
• Authors: Kristina Toutanova, Dan Klein, Christopher D. Manning, Yoram Singer
• Keywords: Conditional Loglinear Models, Dependency Network Representation, Multiple Consecutive Words, NAACL 2003, NAACL 2007