Kristina Toutanova
Christopher D. Manning
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.
- Steven Abney, Robert E. Schapire, and Yoram Singer. 1999. Boosting applied to tagging and PP attachment. In EMNLP/VLC 1999, pages 38--45.Google Scholar
- Thorsten Brants. 2000. TnT -- a statistical part-of-speech tagger. In ANLP 6, pages 224--231. Google ScholarDigital Library
- Eric Brill and Jun Wu. 1998. Classifier combination for improved lexical disambiguation. In ACL 36/COLING 17, pages 191--195. Google ScholarDigital Library
- Eric Brill. 1995. Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics, 21(4):543--565. Google ScholarDigital Library
- Eugene Charniak, Curtis Hendrickson, Neil Jacobson, and Mike Perkowitz. 1993. Equations for part-of-speech tagging. In AAAI 11, pages 784--789.Google Scholar
- Stanley F. Chen and Ronald Rosenfeld. 2000. A survey of smoothing techniques for maximum entropy models. IEEE Transactions on Speech and Audio Processing, 8(1):37--50.Google ScholarCross Ref
- Kenneth W. Church. 1988. A stochastic parts program and noun phrase parser for unrestricted text. In ANLP 2, pages 136--143. Google ScholarDigital Library
- Michael Collins. 2002. Discriminative training methods for Hidden Markov Models: Theory and experiments with perceptron algorithms. In EMNLP 2002. Google ScholarDigital Library
- Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, and David J. Spiegelhalter. 1999. Probabilistic Networks and Expert Systems. Springer-Verlag, New York. Google ScholarDigital Library
- David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, and Carl Myers Kadie. 2000. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1(1):49--75. Google ScholarDigital Library
- Mark Johnson, Stuart Geman, Stephen Canon, Zhiyi Chi, and Stefan Riezler. 1999. Estimators for stochastic "unification-based" grammars. In ACL 37, pages 535--541. Google ScholarDigital Library
- Dan Klein and Christopher D. Manning. 2002. Conditional structure versus conditional estimation in NLP models. In EMNLP 2002, pages 9--16. Google ScholarDigital Library
- John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML-2001, pages 282--289. Google ScholarDigital Library
- Sang-Zoo Lee, Jun ichi Tsujii, and Hae-Chang Rim. 2000. Part-of-speech tagging based on Hidden Markov Model assuming joint independence. In ACL 38, pages 263--169. Google ScholarDigital Library
- Mitchell P. Marcus, Beatrice Santorini, and Mary A. Marcinkiewicz. 1994. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19:313--330. Google ScholarDigital Library
- Ian Marshall. 1987. Tag selection using probabilistic methods. In Roger Garside, Geoffrey Sampson, and Geoffrey Leech, editors, The Computational analysis of English: a corpus-based approach, pages 42--65. Longman, London.Google Scholar
- Adwait Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In EMNLP 1, pages 133--142.Google Scholar
- Scott M. Thede and Mary P. Harper. 1999. Second-order hidden Markov model for part-of-speech tagging. In ACL 37, pages 175--182. Google ScholarDigital Library
- Kristina Toutanova and Christopher Manning. 2000. Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In EMNLP/VLC 1999, pages 63--71. Google ScholarDigital Library
- Tong Zhang and Frank J. Oles. 2001. Text categorization based on regularized linear classification methods. Information Retrieval, 4:5--31. Google ScholarDigital Library
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