The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A hierarchical Bayesian language model based on Pitman-Yor processes
Open accessAbstract
We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods for n-gram language models. Experiments verify that our model gives cross entropy results superior to interpolated Kneser-Ney and comparable to modified Kneser-Ney.
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
A neural probabilistic language model
BengioYoshua, DucharmeRéjean, VincentPascal, & JanvinChristian (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944966 · Cited by 2,644
Hierarchical Dirichlet Processes
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000302 · Cited by 2,120
Self-citationAn empirical study of smoothing techniques for language modeling
Chen, S. F., & Goodman, J. (1999) · Computer Speech & Language · doi:10.1006/csla.1999.0128 · Cited by 2,066
Gibbs Sampling Methods for Stick-Breaking Priors
Ishwaran, H., & James, L. F. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501750332758 · Cited by 1,511
Improved backing-off for M-gram language modeling
Kneser, R., & Ney, H. (2002) · 1995 International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1995.479394 · Cited by 1,473
The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator
Pitman, J., & Yor, M. (1997) · The Annals of Probability · doi:10.1214/aop/1024404422 · Cited by 1,202
Probabilistic Inference Using Markov Chain Monte Carlo Methods
Neal, R. M. (2011) · Cited by 1,040
Combinatorial Stochastic Processes
(2006) · Lecture Notes in Mathematics · doi:10.1007/b11601500 · Cited by 935
Two decades of statistical language modeling: where do we go from here?
Rosenfeld, R. (2000) · Proceedings of the IEEE · doi:10.1109/5.880083 · Cited by 735
Cited by
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., & Robinson, T. (2013) · arXiv · doi:10.48550/arxiv.1312.3005 · Cited by 1,051
(2014) · doi:10.1145/2635868.2635901 · Cited by 159
Symbol emergence in robotics: a survey
Taniguchi, T., Nagai, T., Nakamura, T., Iwahashi, N., Ogata, T., & Asoh, H. (2016) · Advanced Robotics · doi:10.1080/01691864.2016.1164622 · Cited by 150
Metadata
- Authors
- Teh, Y. W.
- Venue
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
- Published
- 2006
- Pages
- 985-992
- OpenAlex
- W2154099718
- Cited by
- 489
- References
- 12
- FWCI
- 8.64
- Type
- conference paper
- Language
- en
- Open access
- gold
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