Improving Query Classification by Features’ Weight Learning

Query Classification;Weight learning;Electrical and Computer Engineering

Abghari, Arash

University of Waterloo

关键词: Query Classification;    Weight learning;    Electrical and Computer Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/7484/1/Abghari_Arash.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

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.