dc.contributor.author | Abghari, Arash | |
dc.date.accessioned | 2013-04-30 13:35:46 (GMT) | |
dc.date.available | 2013-04-30 13:35:46 (GMT) | |
dc.date.issued | 2013-04-30T13:35:46Z | |
dc.date.submitted | 2013 | |
dc.identifier.uri | http://hdl.handle.net/10012/7484 | |
dc.description.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. | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | Query Classification | en |
dc.subject | Weight learning | en |
dc.title | Improving Query Classification by Features’ Weight Learning | en |
dc.type | Master Thesis | en |
dc.pending | false | en |
dc.subject.program | Electrical and Computer Engineering | en |
uws-etd.degree.department | Electrical and Computer Engineering | en |
uws-etd.degree | Master of Applied Science | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |