Features' Weight Learning towards Improved Query Classification

Arash Abghari, Kacem Abida, Fakhri Karray

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.