Learning a syntagmatic and paradigmatic structure from language data with a bi-multigram model

Proceedings of the Annual Meeting of the Association for Computational Linguistics

Volumn 1, Issue , 1998, Pages 300-306

  Deligne, Sabine ^a   Sagisaka, Yoshinori ^a  

^a Dept1   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MODELING LANGUAGES; NATURAL LANGUAGE PROCESSING SYSTEMS; STOCHASTIC SYSTEMS;

BIGRAMS; CLUSTERING PROCEDURE; FULLY INTEGRATED; LANGUAGE MODEL; MODELLING TOOLS; STOCHASTIC LANGUAGE MODELS; TRI GRAMS; VARIABLE LENGTH;

COMPUTATIONAL LINGUISTICS;

EID: 84893639419     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. F. Bimbot, R. Pieraccini, E. Levin, and B. Atal. 1995. Variable-length sequence modeling: Multigrams. IEEE Signal Processing Letters, 2(6), June.
2. P. F. Brown, V. J. Delia Pietra, P. V. de Souza, J. C. Lai, and R. L. Mercer. 1992. Class-based n-gram models of natural language. Computational Linguistics, 18 (4) :467-479.
3. P. Clarkson and R. Rosenfeld. 1997. Statistical language modeling using the cmu-cambridge toolkit. Proceedings of EUROSPEECH 97.
4. S. Deligne and F. Bimbot. 1995. Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams. Proceedings of ICASSP 95.
5. S. Deligne, F. Yvon, and F. Bimbot. 1996. Introducing statistical dependencies and structural constraints in variable-length sequence models. In Grammatical Inference: Learning Syntax from Sentences, Lecture Notes in Artificial Intelligence 1147, pages 156-167. Springer.
6. A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977. Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistics Society, 39 (1) :l-38.
7. F. Jelinek and R. L. Mercer. 1980. Interpolated estimation of markov source parameters from sparse data. Proceedings of the workshop on Pattern Recognition in Practice, pages 381-397.
8. S. M. Katz. 1987. Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Trans. on Acoustic, Speech, and Signal Processing, 35 (3) :400-401, March.
9. T. Kawahara, S. Doshita, and C. H. Lee. 1997. Phrase language models for detection and verification-based speech understanding. Proceedings of the 1997 IEEE workshop on Automatic Speech Recognition and Understanding, pages 49-56, December.
10. H. Masataki and Y. Sagisaka. 1996. Variableorder n-gram generation by word-class splitting and consecutive word grouping. Proceedings of ICASSP 96.
11. S. Matsunaga and S. Sagayama. 1997. Variablelength language modeling integrating global constraints. Proceedings of EUROSPEECH 97.
12. K. Ries, F. D. Buo, and A. Waibel. 1996. Class phrase models for language modeling. Proceedings of ICSLP 96.
13. M. Siu. 1998. Learning local lexical structure in spontaneous speech language modeling. Ph. D. thesis, Boston University.
14. B. Suhm and A. Waibel. 1994. Towards better language models for spontaneous speech. Proceedings of ICSLP 94.
15. I. H. Witten and T. C. Bell. 1991. The zero-frequency problem: estimating the probabilities of novel events in adaptative text compression. IEEE Trans. on Information Theory, 37(4):1085-1094, July.