Paper: Feature-Rich Part-Of-Speech Tagging With A Cyclic Dependency Network

ACL ID N03-1033
Title Feature-Rich Part-Of-Speech Tagging With A Cyclic Dependency Network
Venue Human Language Technologies
Session Main Conference
Year 2003
Authors

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag con- texts via a dependency network representa- tion, (ii) broad use of lexical features, includ- ing jointly conditioning on multiple consecu- tive words, (iii) effective use of priors in con- ditional 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.