Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Modern Information Retrieval
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Language modeling with sentence-level mixtures
HLT '94 Proceedings of the workshop on Human Language Technology
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Design and implementation of a Bayesian network speech recognizer
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Combining topic specific language models
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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We present a language model implemented with dynamic Bayesian networks that combines topic information and structure information to capture long distance dependencies between the words in a text while maintaining the robustness of standard n -gram models. We show that the model is an extension of sentence level mixture models, thereby providing a Bayesian explanation for these models. We describe a procedure for unsupervised training of the model. Experiments show that it reduces perplexity by 13% compared to an interpolated trigram.