On the learnability and usage of acyclic probabilistic finite automata
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Building probabilistic models for natural language
Building probabilistic models for natural language
Smoothing Probabilistic Automata: An Error-Correcting Approach
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Stochastic Grammatical Inference with Multinomial Tests
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Shallow Parsing Using Probabilistic Grammatical Inference
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Improving Probabilistic Grammatical Inference Core Algorithms with Post-processing Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Finite Automata for Compact Representation of Language Models in NLP
CIAA '01 Revised Papers from the 6th International Conference on Implementation and Application of Automata
On the Convergence Rate of Good-Turing Estimators
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Multi-site data collection for a spoken language corpus
HLT '91 Proceedings of the workshop on Speech and Natural Language
Learning Partially Observable Markov Models from First Passage Times
ECML '07 Proceedings of the 18th European conference on Machine Learning
On Growing and Pruning Kneser–Ney Smoothed -Gram Models
IEEE Transactions on Audio, Speech, and Language Processing
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In statistical language modelling the classic model used is n -gram. This model is not able however to capture long term dependencies, i.e. dependencies larger than n . An alternative to this model is the probabilistic automaton. Unfortunately, it appears that preliminary experiments on the use of this model in language modelling is not yet competitive, partly because it tries to model too long term dependencies. We propose here to improve the use of this model by restricting the dependency to a more reasonable value. Experiments shows an improvement of 45% reduction in the perplexity obtained on the Wall Street Journal language modeling task.