Features' weight learning towards improved query classification

Authors:
Arash Abghari;Kacem Abida;Fakhri Karray
Affiliations:
Electrical and Computer Engineering Department, University of Waterloo, Waterloo, Ontario, Canada;Electrical and Computer Engineering Department, University of Waterloo, Waterloo, Ontario, Canada;Electrical and Computer Engineering Department, University of Waterloo, Waterloo, Ontario, Canada
Venue:
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Year:
2012

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Abstract

This paper is an attempt to enhance query classification in call routing applications. We have introduced a new method to learn weights from training data by means of regression model. In this work, we have tested our method with tf-idf weighting scheme, but the approach can be applied to any weighting scheme. Empirical evaluations with several classifiers including Support Vector Machines (SVM), Maximum Entropy, Naive Bayes, and K-Nearest Neighbor (KNN) show substantial improvement in both macro and micro F1 measure.