Site Search
Computer Science


Christie Ezeife, Ph.D.Dr. Christie Ezeife
Dr. Christie Ezeife
Alioune Ngom, Ph.D.Dr. Alioune Ngom
Dr. Alioune Ngom
Dr. Robert KentDr. Robert Kent
Dr. Robert Kent
Robin Gras, Ph.D.Dr. Robin Gras
Dr. Robin Gras
Dr. Ziad Kobti lecturingDr. Ziad Kobti
Dr. Ziad Kobti
Dr. Scott GoodwinDr. Scott Goodwin
Dr. Scott Goodwin
Arunita Jaekel, Ph.D.Dr. Arunita Jaekel
Dr. Arunita Jaekel
Jessica Chen, Ph.D.Dr. Jessica Chen
Dr. Jessica Chen
Lambton TowerLambton Tower
Lambton Tower
Xiaobu Yuan, Ph.D.Dr. Xiaobu Yuan
Dr. Xiaobu Yuan
Dr. Luis RuedaDr. Luis Rueda
Dr. Luis Rueda
Windsor WaterfrontWindsor Waterfront Park
Windsor Waterfront Park
Imran Ahmad, Ph.D.Dr. Imran Ahmad
Dr. Imran Ahmad

Thesis Proposal Announcement: A comparative study of document representation methods

Add this event into your calendar using the iCAL format
  • Fri, 01/18/2019 - 2:30pm - 4:30pm

A comparative study of document representation methods


MSc Thesis Proposal by:

Ziyang Tian
Date:  Friday, January 18th, 2018
Time:  2:30 pm – 4:30 pm
Location: 3105, Lambton Tower
Representation learning is crucial for downstream machine learning tasks such as classification. This thesis conducts a comparative study of document representation methods, in particular the comparison between traditional TF-IDF based-methods, dimensionality reduction methods based on matrix factorization, and more recent neural network based methods. The methods will be evaluated extensively on text data from different areas with different document length. We also propose a new representation method that improves the TF/IDF method.

Thesis Committee:

Internal Reader: Dr. Alioune Ngom

External Reader: Dr. Behnam Shahrrava

Advisor: Dr. Jianguo Lu


Christine Weisener
(519)253-3000 ext.3716