Improving Query Classification by Features’ Weight Learning
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Date
2013-04-30T13:35:46Z
Authors
Abghari, Arash
Advisor
Journal Title
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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