Feature-rich part-of-speech tagging with a cyclic dependency network
2003Vol. 1, pp. 173–180
Citations Over TimeTop 1% of 2003 papers
Abstract
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.
Related Papers
- → Parsing Discontinuous Phrase Structure with Grammatical Functions(2008)26 cited
- Indonesian Dependency Treebank: Annotation and Parsing(2012)
- Phrase Structure Parsing with Dependency Structure(2010)
- Exploring Treebank Transformations in Dependency Parsing(2009)
- → Cross-lingual dependency parsing for closely related languages - Helsinki's submission to VarDial 2017(2017)4 cited