The week's pick
Reseach Article
Adaptive Hybrid POS Cache based Semantic Language Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 13 |
| Year of Publication: 2012 |
| Authors: Manzoor Ahmad Chachoo, S. M. K. Quadri |
10.5120/4878-7310
|
Manzoor Ahmad Chachoo, S. M. K. Quadri . Adaptive Hybrid POS Cache based Semantic Language Model. International Journal of Computer Applications. 39, 13 ( February 2012), 7-10. DOI=10.5120/4878-7310
Abstract
This paper presents a language model as an improvement over the stochastic language model for developing a syntactic structure based on word dependencies in local and non local domain. The model copes with the issues of limited amount of training material and the exploitation of the linguistic constraints of the language. The proposed model is a dynamic probabilistic model which uses word dependencies based on their part of speech tags along with the tri-gram Model but also takes care of the influence of the word which are very far from the word being considered in a text and stores the word history in a dynamic cache for information mining using long distance dependency. The model based on second order Hidden Markov Model has been used and an improvement of 2% has been observed in the word error rate and 4% reduction in the perplexity when compared to the normal tri-gram model.
References
- L.R. Bahl, F. Jelinek and R.L.Mercer. 1983 . A Maximum Likelihood Approach to Continuous Speech Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 5, pages 179-190,
- Roger Argiles Solsona, Eric Fosler-Lussier, Hong-Kwang J. Kuo, Alexandros Potamianos, Imed Zitouni, 2002, Adaptive language models for spoken dialogue systems” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
- E. Fosler-Lussier and H.-K. J. Kuo,2001, Using semantic class information for rapid development of language models within ASR dialogue systems,” in Proc. ICASSP, Salt Lake City, Utah.
- P.F. Brown, V.J. Della Pietra, P.V. DeSouza, J.C. Lai, and R.L. Mercer, 1992 Class-based n-gram models of natural language, Computational Linguistics, vol. 18, no. 4, pp. 467–479.
- P. Dumouchel, V.Gupta, M. Lennig & P. Mermelstein. 1988 , Three Probablisitic Language Models for a Large Vocabulary Speech Recognizer, Proceedings of International Conference on Acoustics Speech and Signal Processesing.
- K. Shikano, 1987, Improvement of Word Recognition Results by Trigram Model Proceedings of International Conference on Acoustics Speech and Signal Processesing.
- A.M. Derouault, B. Merialdo. 1986 , Natural Language Modeling for Phoneme to Text Transcription , IEEE Transactions on Pattern Analysis and Machine Intelligence Vol PAMI-8 No. 6.
- F. Jelinek, B. Merialdo, S. Roukos, and M. Strauss, 1991 , A Dynamic Language Model for Speech Recognition” In the Proceedings of the workshop on Speech and Natural Language, Association for Computational Linguistics Stroudsburg, PA, USA .
- Kuhn, R., 1988 ,Speech Recognition and the Frequency of Recently Used Words: a Modified Markov Model for Natural Language, Proceedings of COLING Budapest, Vol. 1, pp. 348-350.
- Kupiec, Julian , 1989 ,Probabilistic Models of Short and Long Distance Word Dependencies in Running Text’, Proceedings of Speech and Natural Language DARPA Workshop, pp. 290-295.
- L.R. Rabiner, S.E. Levinson, and M.M. Sondhi, 1983. An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition. Bell System Technical Journal, Vol. 62, No.4, April. pp 1035-1074.
- Scott M. Thede , Mary P. Harper , 1999 , A second-order Hidden Markov Model for part-of-speech tagging’,In the Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics..
- L.E. Baum., 1972 ,An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of a Markov Process. Inequalities, 3,. pp. 1-8.
- A. J. Viterbi. , 1967 , Error Bounds for Convolutional Codes and an Asymptotically Optimal Decoding Algorithm. IEEE Trans. on Information Theory Vol. IT-13, April. pp. 260-269.
- F. Jelinek. 1991 , Up from trigrams ! the struggle for improved language models, in Proc EUROSPEECH pp 1937-1040
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