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.
Toutanova et al. demonstrated the use of both preceding and following tag contexts via a dependency network representation and made use of some additional features such as lexical features including jointly conditioning on multiple consecutive words and other fine-grained modeling of word features REF .
Feature-Rich Part-Of-Speech Tagging With A Cyclic Dependency Network
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science