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. 