A fast method for statistical grammar induction

Natural Language Engineering

Volumn 4, Issue 3, 1998, Pages 191-209

  Hogenhout, Wide R ^a   Matsumoto, Yuji ^a  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84937179256     PISSN: 13513249     EISSN: 14698110     Source Type: Journal    
DOI: 10.1017/S1351324998001983     Document Type: Article

References (22)

1. Baker, J. K. (1979) Trainable grammars for speech recognition. In Speech Communication Papers for the 97th Meeting of the Acoustical Society of America, D. H. Klatt and J. J. Wolf (eds.), pp. 547–550.
2. Black, E., Abney, S., Flickenger, D., Gdaniec, C., Grishman, R., Harrison, R, Hindle, D., Ingria, R., Jelinek, F., Klavans, J., Fiberman, M., Marcus, M., Roukos, S., Santorini, B. and Strzalkowski, T. (1991) A procedure for quantitatively comparing the syntactic coverage of English grammars. Proceedings of the Workshop on Speech and Natural Language, Defense Advanced Research Projects Agency, U.S. Govt., pp. 306–311.
3. Black, E., Garside, R. and Leech G. (1993) Statistically-Driven Computer Grammars of English: The IBM/Lancaster Approach. Rodopi.
4. Briscoe, T. and Waegner, N. (1992) Robust stochastic parsing using the inside-outside algo-rithm. Workshop Notes, Statistically-Based NLP Techniques, AAAI, pp. 33–41.
5. Brown, P. F., Della Pietra, S. A., deSouza, P. V., Lai J. C. and Mercer, R. L. (1992) Class-based n-gram models of natural language. Computational Linguistics, 18(4): 467–479.
6. Carroll, G. and Charniak, E. (1992) Learning probabilistic dependency grammars from labelled text. Working Notes, Fall Symposium Series, AAAI, pp. 25–32.
7. Carroll, G. and Charniak, E. (1992) Two experiments on learning probabilistic dependency grammars from corpora. Workshop Notes, Statistically-Based Natural Language Program-ming Techniques, AAAI, pp. 1–13.
8. Cover, T. M. and Thomas, J. A. (1991) Elements of Information Theory. Wiley.
9. Dempster, A. P., Laird N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B, 39: 1–38.
10. Elman, J. L. (1991) Incremental learning, or the importance of starting small. Proceedings 13th Annual Conference of the Cognitive Science Society, pp. 443–448.
11. Fujisaki, T., Jelinek, F., Cocke, J., Black, E. and Nishino, T. (1989) A probabilistic method for sentence disambiguation. Proceedings of the 1st International Workshop on Parsing Technologies, pp. 105–114.
12. Gross, P. K. (1988) Incremental multiple concept learning using experiments. Proceedings 5th International Conference on Machine Learning.
13. Hogenhout, W. R. and Matsumoto, Y. (1996) Training stochastical grammars on semantical categories. In Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, E. Riloff S. Wermter and G. Scheler (eds.), pp. 160–172. Springer-Verlag.
14. Kita, K. (1996) Mixture probabilistic context-free grammar. J. Natural Language Processing, 3(4): 103–113.
15. Klasner, N. and Simon, H. U. (1995) From noise-free to noise-tolerant and from on-line to batch learning. Proceedings 8th Annual Conference on Computational Learning Theory, pp. 250–257.
16. Lan K. and Young, S. J. (1990) The estimation of stochastic context-free grammars using the Inside-Outside Algorithm. Computer Speech and Language, 4: 35–56.
17. Lan K. and Young, S. J. (1991) Applications of stochastic context-free grammars using the Inside-Outside algorithm. Computer Speech and Language, 5: 237–257.
18. Pereira, F. and Schabes, Y. (1992) Inside Outside reestimation from partially bracketed corpora. Proceedings 30th Annual Meeting of the Association for Computational Linguistics, pp. 128–135.
19. Reinke, R. E. and Michalkski, R. S. (1988) Incremental learning of decision rules: A method and experimental results. In Machine Intelligence, J. E. Hayes, D. Michie and J. Richards (eds.). Oxford University Press.
20. Schabes, Y., Roth, M. and Osborne, R. (1993) Parsing the Wall Street Journal with the Inside-Outside algorithm. Proceedings of the 6th Conference of the European Chapter of the Association for Computational Linguistics, pp. 341–347.
21. Sharman, R., Jelinek, F. and Mercer, R. (1990) Generating a grammar for statistical training. Proceedings of the DARPA Speech and Natural Language Workshop, pp. 267–274.
22. Winston, P. H. (1975) Learning structural descriptions from examples. In The Psychology of Computer Vision, P. H. Winston (ed.). McGraw Hill.