Title: Natural Language Call Routing: Towards Combination and Boosting of Classifiers

Authors: Imed Zitouni, Hong-Kwang Jeff Kuo, Chin-Hui Lee

Abstract: In this paper, we describe different techniques to improve natural language call routing: boosting, relevance feedback, discriminative training, and constrained minimization. Their common goal is to reweight the data in order to let the system focus on documents judged hard to classify by a single classifier. These approaches are evaluated with the common vector-based classifier and also with the beta classifier which had given good results in the similar task of E-mail steering. We explore ways of deriving and combining uncorrelated classifiers in order to improve accuracy. Compared to the cosine and beta baseline classifiers, we report an improvement of 49% and 10%, respectively.

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