Discriminative Training of Naïve Bayes Classifiers for Natural Language Call Routing

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 30% error reduction over our best MLtrained system. The proposed method is also compared with the vector-based method used previously by others in call routing task. The comparative results clearly show that NBCs after DT can outperform the vector-based technique.

By: Pengfei Liu, Hui Jiang, Imed Zitouni

Published in: RC23358 in 2004


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