Task adaptation in stochastic language model for Chinese homophone disambiguation

ACM Transactions on Asian Language Information Processing

Volumn 2, Issue 1, 2003, Pages 49-62

  Lee, Yue Shi ^a  

^a MING CHUAN UNIVERSITY   (Taiwan)

Author keywords

Adaptive learning; Chinese homophone disambiguation; Language model; Neural network; Runtime application domain; Task adaptation

Indexed keywords

ADAPTIVE ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; FEEDBACK; GLOBAL OPTIMIZATION; INFORMATION ANALYSIS; LEARNING ALGORITHMS; NEURAL NETWORKS; PROBABILITY; RANDOM PROCESSES; ROBUSTNESS (CONTROL SYSTEMS);

ADAPTIVE LEARNING; CHINESE HOMOPHONE DISAMBIGUATION; LANGUAGE MODELS; RUNTIME APPLICATION DOMAIN; TASK ADAPTATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 3042832032     PISSN: 15300226     EISSN: None     Source Type: Journal    
DOI: 10.1145/964161.964164     Document Type: Article

References (19)

1. BROWN, P. F., ET AL. 1992. Class-based N-gram models of natural language. Comput. Linguist. 18, 467-479.
2. CHURCH, K.W., AND GALE, W.A. 1991. A comparison of the enhanced good-Turing and deleted estimation methods for estimating probabilities of English bigrams. Comput. Speech Lang. 5, 19-54.
3. nd Annual Meeting of ACL. 272-278.
4. ESSEN, U. AND STEINBISS, V. 1992. Cooccurrence smoothing for stochastic language modeling. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. I-161 -I-164.
5. FURUL, S. ET AL. 1992. Recent topics in speech recognition research at NTT laboratories. In Proceedings of Speech and Natural Language Workshop. 162-167.
6. GOOD, I. J. 1953. The population frequencies of species and the estimation of population parameters. Biometrika 40. 237-264.
7. KATZ, S. M. 1987. Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Trans. Acoustic, Speech, Signal Process. 35. 400-401.
8. KONCAR, N. AND GUTHRIE, G. 1994. A natural language translation neural network. In Proceedings of the International Conference on New Methods in Language Processing. 71-77.
9. KUHN, R. AND MORI, R. D. 1990. A cache-based natural language model for speech recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12, 570-583.
10. KUHN, R. AND MORI, R. D. 1992. Corrections to a cache-based natural language model for speech recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14. 691-692.
11. LIN, Y. C. ET AL. 1994. Automatic model refinement: with an application to tagging. In Proceedings of the International Conference on Computational Linguistics. 148-153.
12. MATSUNAGA, S. ET AL. 1992. Task adaptation in stochastic language models for continuous speech recognition. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. I-165-I-168.
13. NAKAMURA, M. ET AL. 1990. Neural network approach to word category prediction for English texts. In Proceedings of the International Conference on Computational Linguistics. 213-218.
14. st Annual Meeting of ACL. 183-190.
15. PLACEWWAY, P. ET AL. 1993. The estimation of powerful language models from small and large corpora. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. II-33-II-36.
16. SCHMID, H. 1987. Part-of-speech tagging with neural networks. In Proceedings of the International Conference on Computational Linguistics. 172-176.
17. SHKANO, K. 1987. Improvement of word recognition results by trigram model. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 1261-1264.
18. SPROAT, R. 1990. An application of statistical optimization with dynamic programming to phonemic-input-to-character conversion for Chinese. In Proceedings of the ROCLING Conference. 377-390.
19. WONG, S.K.M. AND CAI, Y. J. 1993. Computation of term associations by a neural network. In Proceedings of the International ACMSIGIR Conference on Research and Development in Information Retrieval. 107-115.