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Augmenting Naive Bayes Classifiers With Statistical Language Models, Fuchun Peng
Augmenting Naive Bayes Classifiers With Statistical Language Models, Fuchun Peng
Computer Science Department Faculty Publication Series
We augment naive Bayes models with statistical n-gram language models to address short- comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we re- fer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the indepen- dence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straight- forward application of sophisticated smoothing techniques …