Part of speech tagging in context

COLING 2004 - Proceedings of the 20th International Conference on Computational Linguistics

Volumn , Issue , 2004, Pages

  Banko, Michele ^a   Moore, Robert C ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPREHENSIVE COMPARISONS; HMM TRAINING; IN CONTEXTS; LEXICAL PROBABILITY; PART OF SPEECH TAGGING; PARTS-OF-SPEECH TAGGING; STATE OF THE ART; UNSUPERVISED METHOD;

COMPUTATIONAL LINGUISTICS;

EID: 85119091952     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. L.R. Bahl, F. Jelinek, and R. Mercer. 1983. A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(2):179-190.
2. L.E. Baum, T. Petrie, G. Soules, and N. Weiss. A maximization technique in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics, 41:164-171.
3. E. Brill. 1992. A simple rule-based part of speech tagger. In Proceedings of the Third Conference on Applied Natural Language Processing, ACL. Trento, Italy.
4. E. Brill. 1995. Unsupervised learning of disambiguation rules for part of speech tagging. In Proceedings of the Third Workshop on Very Large Corpora, Cambridge, MA.
5. K. Church. 1998. A stochastic parts program and noun phrase parser for unrestricted text. In Second Conference on Applied Natural Language Processing, ACL.
6. M. Collins. 2002. Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, PA.
7. D. Cutting, J. Kupiec, J. Pedersen and P. Sibun. 1992. A practical part-of-speech tagger. In Third Conference on Applied Natural Language Processing. ACL.
8. D. Elworthy. 1994. Does Baum-Welch re-estimation help taggers. In Proceedings of the Fourth Conference on Applied Natural Language Processing, ACL.
9. J. Kupiec. 1992. Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language 6.
10. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML-01, pages 282-289.
11. B. Merialdo. 1991. Tagging English text with a probabilistic model. In Proceedings of ICASSP. Toronto, pp. 809-812.
12. H. Ney, U. Essen and R. Kneser. 1994. On structuring probabilistic dependencies in stochastic language modeling. Computer, Speech and Language, 8:1-38.
13. A. Ratnaparkhi. 1998. Unsupervised statistical models for prepositional phrase attachment. In Proceedings of the Seventeenth International Conference on Computational Linguistics. Montreal, Canada.
14. K. Toutanova, D. Klein, C. Manning, and Y. Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL. pp. 252-259.
15. A.J. Viterbi. 1967. Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Transactions on Information Theory, 13:260-269.
16. S. Young. 1999. Acoustic modelling for large vocabulary continuous speech recognition. Computational Models of Speech Pattern Processing: Proc NATO Advance Study Institute. K. Ponting, Springer-Verlag: 18-38.