Xiaodan Zhu
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I am a researcher of National Research Council Canada, a research organiation that has just passed its 100th birthday, and proudly 10 Nobel Laureates have worked as full-time researchers in NRC during the time. My research interests include natural language processing, spoken document understanding, and machine learning. I received my Ph.D. from the Department of Computer Science of the University of Toronto in 2010 and my Masters of Engineering from the Department of Computer Science and Technology of Tsinghua University in 2000. Before, I have also worked with various industry labs, either as a full-time researcher (Intel's China Research Center), visiting scholar (Microsoft Research Asia), or as a research intern (IBM T.J. Watson Research Center; Google Inc).
Researcher, National Research Council Canada
Adjunct Professor, EECS, University of Ottawa
+1 (613) 993-0646
Natural Language Processing, Social Media, Medical Informatics, Big Data;

Deep Learning, Machine Learning.

Recent highlights
Tree LSTM: we proposed tree LSTM, which was published at the International Conference on Machine Learning; see the paper at [ICML], or at [ArXiv] for an earlier version.
Sentiment Analysis for Tweets: we built top-ranked systems in a number of SEMEVAL competitions on sentiment analysis for tweets, see here for details, and see publications for papers.
Our paper on semantic compositionality was awarded the Adam Kilgarriff *SEM Best Paper Award for Lexical Semantics at the Joint Conference on Lexical and Computational Semantics (*SEM), Denver, Colorado. [paper]

I will give a tutorial (together with Edward Grefenstette, Google DeepMind) on "deep learning for semantic compostion" at ACL-2017. [pdf] [txt]

From July 1, 2016 to June 30, 2019, I will serve a three-year term (part-time) for NSERC (Natural Sciences and Engineering Research Council of Canada) as an Evaluation Group (EG) Member to evaluate Discovery Grants applications in the area of Computer Science (link).

Employing our Tree-LSTM, Qian Chen's recent work on Natural Language Inference (paper link) has achieved the the state-of-the-art results on the Stanford Natural Language Inference benchmark (link). (Qian is a vistinting student I co-supervised. He will visit me and Diana Inkpen at University of Ottawa.)

Our paper exploring neural nets for semantic compositionality, "DAG-Structured Recurrent Neural Networks for Semantic Compositionality" has been published at NAACL-2016 (link).

On Feb. 22, 2016, I gave a talk at Ottawa Machine Learning Meetup on "Deep Learning for Text Mining: Case Studies on Social Media and Medical Text" (talk announcement).

Between July and November 2015, I gave a couple of talks at the University of Toronto, McGill University, UMass medical school, University of Ottawa, and Baidu Inc. about our recent efforts on neural networks for semantics.
Our work on long short-term memory over tree structures has been accepted to International Conference on Machine Learning. Here is the ICML version with updated results [ICML]. The older arXiv version can be found at [ArXiv];
Our paper presented at NIPS-2014 Workshop on Representation and Learning Methods for Complex Outputs [paper]
Gave a tutorial at EMNLP-2014 in Doha on "Sentiment Analysis of Social Media Texts."
In Semeval-2014 Task 9: Sentiment Analysis in Twitter, we ranked first in five of the ten subtask-domain combinations among about 40 teams. In Semeval-2014 Task 4: Aspect Based Sentiment Analysis, our models ranked first in three of the six subtasks among about 30 teams. [paper-1][2]
Our ACL-2014 paper on sentiment analysis (negation modeling). [paper]
Our paper on machine translation of sentiment, presented at EACL-2014. [paper]