Yet another job talk on Monday.

---------- Forwarded message ---------- From: Pierre McKenzie mckenzie@iro.umontreal.ca Date: 2017-02-16 20:03 GMT-05:00 Subject: Colloque DIRO, lundi 20 février, Xiaodan Zhu (NRC et U Ottawa) To: seminaires@iro.umontreal.ca

*Deep Learning for Meaning in Natural Language*

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* Xiaodan Zhu *

University of Ottawa

*Lundi 20 février, 15:30-16:30*, *Salle 6214*, Pavillon André-Aisenstadt

Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30

* Cette conférence sera donnée en anglais.*

*Résumé:*

Text and speech are ubiquitous in our life and are essentially part of the big data we are facing. Developing machine learning models to understand and analyze such data should continue to have significant values. However, modeling human language involves many hard problems in artificial intelligence and is very challenging. this talk I will present our recent deep learning models for learning and representing the meaning of natural language, spanning from words to documents, which involve representing the basic components (e.g., words), compositionality (representing sentences), and document-level modeling. I will present some state-of-the-art results we have achieved on a range of problems and discuss the limitations of the current models. In addition, I would like to briefly discuss some important applications in medical information extraction, social media analysis, and spoken document understanding.

*Biographie:*

Xiaodan Zhu is a Researcher of National Research Council Canada and an Adjunct Professor of EECS, University of Ottawa. His research interests include natural language processing, deep learning, social media analysis, medical information extraction, and spoken language understanding. Xiaodan received his Ph.D. from the Department of Computer Science at the University of Toronto in 2010 and M.S. from the Department of Computer Science of Tsinghua University in 2000. Xiaodan has research experience with IBM T.J. Watson Research Center, Google, Intel, and Microsoft.