Discriminative training of naive bayes classifiers for natural language call routing

Abstract

In this paper, we propose to use a discriminative training(DT) method to improve naive Bayes classifiers in context of natural language call routing. As opposed to the traditional maximum likelihood estimation, all conditional probabilties in Naive Bayes classifers (NBC) are estimated discriminatively based on the minimum classification error (MCE) criterion. A smoothed classification error rate in training set is formulated as an objective function and the GPD (generalized probabilistic descent) method is used to minimize the objective function with respect to all conditional probabilities in NBCs. Two versions of NBC are used in this work. In the first version all NBCs corresponding to various destinations use the same word feature set while destination-dependent feature set is chosen for each destination in the second version. Experimental results on a banking call routing task show that the discriminative training method can achieve up to about 31% error reduction over our best ML-trained system. The proposed formulation is applicable to other algorithms addressing a wide range of tasks, such as topic identification, information retrieval and speech understanding.