Bootstrapping without the boot

HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Volumn , Issue , 2005, Pages 395-402

  Eisner, Jason ^a   Karakos, Damianos ^a  

^a Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS; STRAPPING;

LEARNING PROCESS; SUPERVISED METHODS; UNSUPERVISED TECHNIQUES;

LEARNING SYSTEMS;

EID: 47349127077     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1220575.1220625     Document Type: Conference Paper

References (22)

1. R. K. Ando and T. Zhang. 2005. A high-performance semisupervised learning method for text chunking. In ACL.
2. J. K. Baker. 1979. Trainable grammars for speech recognition. In Jared J. Wolf and Dennis H. Klatt, editors, Speech Communication Papers Presented at the 97th meeting of the Acoustical Society of America, MIT, Cambridge, MA, June.
3. A. Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Proc. of COLT, July.
4. P. F. Brown, J. Cook, S.A. Della Pietra, V.G. Della Pietra, F. Jelinek, J.D. Lafferty, R.L. Mercer, and P.S. Roossin. 1990. A statistical approach to machine translation. CL, 16(2).
5. N. Chomsky. 1981. Lectures on Government and Binding. Foris, Dordrecht.
6. M. Collins and Y. Singer. 1999. Unsupervised models for named entity classification. In Proc. of EMNLP/VLC.
7. S. Cucerzan and D. Yarowsky. 1999. Language independent named entity recognition combining morphological and contextual evidence. In Proc. of EMNLP/VLC.
8. S. Cucerzan and D. Yarowsky. 2003. Minimally supervised induction of grammatical gender. In Proc. of HLT/NAACL.
9. A. P.Dempster, N.M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B, 39(1):1-38.
10. W. A. Gale, K.W. Church, and D. Yarowsky. 1992a. One sense per discourse. In Proc. of the 4th DARPA Speech and Natural Language Workshop, pages 233-237.
11. W. A. Gale, K. W. Church, and D. Yarowsky. 1992b. Using bilingual materials to develop word sense disambiguation methods. In Proc. of the 4th International Conf. on Theoretical and Methodological Issues in Machine Translation.
12. W. A. Gale, K. W. Church, and D. Yarowsky. 1992c. Work on statistical methods for word sense disambiguation. In Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, pages 54-60.
13. T. Gaustad. 2001. Statistical corpus-based word sense disambiguation: Pseudowords vs. real ambiguous words. In Proc. of ACL-EACL.
14. T. Joachims. 1999. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning. MIT Press.
15. S. M. Katz. 1996. Distribution of context words and phrases in text and language modelling. NLE, 2(1):15-59.
16. I. Dan Melamed. 1997. A word-to-word model of translational equivalence. In Proc. of ACL/EACL, page 490.
17. P. Nakov and M. Hearst. 2003. Category-based pseudowords. In HLT-NAACL'03, pages 67-69, Edmonton, Canada.
18. E. Riloff, J. Wiebe, and T. Wilson. 2003. Learning subjective nouns using extraction pattern bootstrapping. In Proc. of CoNLL, pages 25-32, May-June.
19. H. Schütze. 1998. Automatic word sense discrimination. Computational Linguistics, 23.
20. S. Wang, R. Rosenfeld, Y. Zhao, and D. Schuurmans. 2002. The latent maximum entropy principle. In Proc. of ISIT.
21. D. Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proc. of ACL.
22. D. Yarowsky. 1996. Three Machine Learning Algorithms for Lexical Ambiguity Resolution. Ph.D. thesis, U. of Penn.