Improving Query Classification by Features’ Weight Learning
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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.
Cite this version of the work
Arash Abghari (2013). Improving Query Classification by Features’ Weight Learning. UWSpace. http://hdl.handle.net/10012/7484