A maximum entropy approach to natural language processing
Computational Linguistics
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Inducing Features of Random Fields
Inducing Features of Random Fields
Combination of n-grams and Stochastic Context-Free Grammars for language modeling
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Efficient sampling and feature selection in whole sentence maximum entropy language models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Improvement of a Whole Sentence Maximum Entropy Language Model using grammatical features
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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The Maximum Entropy principle (ME) is an appropriate framework for combining information of a diverse nature from several sources into the same language model. In order to incorporate long-distance information into the ME framework in a language model, a Whole Sentence Maximum Entropy Language Model (WSME) could be used. Until now MonteCarlo Markov Chains (MCMC) sampling techniques has been used to estimate the parameters of the WSME model. In this paper, we propose the application of another sampling technique: the Perfect Sampling (PS). The experiment has shown a reduction of 30% in the perplexity of the WSME model over the trigram model and a reduction of 2% over the WSME model trained with MCMC.