Improving Query Classification by Features’ Weight Learning

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Date

2013-04-30T13:35:46Z

Authors

Abghari, Arash

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Publisher

University of Waterloo

Abstract

This work is an attempt to enhance query classification in call routing applications. A new method has been introduced to learn weights from training data by means of a regression model. This work has investigated applying the tf-idf weighting method, but the approach is not limited to a specific method and can be used for any weighting scheme. Empirical evaluations with several classifiers including Support Vector Machines (SVM), Maximum Entropy, Naive Bayes, and k-Nearest Neighbor (k-NN) show substantial improvement in both macro and micro F1 measures.

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Keywords

Query Classification, Weight learning

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