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I am an Assistant Professor in the Department of Computer and Information Science
at the University of Oregon. I obtained my Ph.D. and M.S. degrees in Computer Science from New York University (working with Ralph Grishman and Kyunghyun Cho),
and my B.S. degree in Computer Science from Hanoi University of Science and Technology. I am also a postdoc in the University of Montréal, working with Yoshua Bengio and people in the Montreal Institute for Learning Algorithms.
RESEARCH
My research explores mechanisms to understand human languages for computers so that computers can perform cognitive language-related tasks for us. Among others, I am especially interested in distilling structured information and mining useful knowledge from the massive human-written text of various domains.
Toward this end, our lab employs and designs effective learning algorithms for information extraction and text mining in natural language processing and data mining. We are currently focusing on deep learning algorithms to solve such problems. We are among the first groups that develop deep learning models and demonstrate their effectiveness for information extraction.
We are also targeting other language-related problems with deep learning, including reading comprehension, machine translation, natural language generation, and chatbots.
NEWS
I am currently recruiting one or two graduate students for Fall 2019 to work on interesting projects of natural language processing and deep learning. Interested candidates can email me for more information. The application procedure for graduate students in the Department of Computer and Information Science can be found here.
I am also willing to supervise students at UO who would like to do research on natural language processing, deep learning and the related topics. Please email me if you are interested in this possibility.
PUBLICATIONS
2019
- BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen and Yoshua Bengio
Proceedings of ICLR 2019, New Orleans, Louisiana, May 2019
[PAPER]
- Systematic Generalization: What Is Required and Can It Be Learned?
Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries and Aaron Courville
Proceedings of ICLR 2019, New Orleans, Louisiana, May 2019
[PAPER]
- One for All: Neural Joint Modeling of Entities and Events
Trung Minh Nguyen and Thien Huu Nguyen
Proceedings of AAAI 2019, Honolulu, Hawaii, January 2019
[PAPER]
2018
- A Case Study on Learning a Unified Encoder of Relations
Lisheng Fu, Bonan Min, Thien Huu Nguyen and Ralph Grishman
Proceedings of the 4th Workshop on Noisy User-generated Text (W-NUT) at EMNLP 208
[PAPER]
- Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching
Weiyi Lu and Thien Huu Nguyen
Proceedings of EMNLP 2018, Brussels, Belgium, November 2018
[PAPER]
- Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs
Minh Nguyen and Thien Huu Nguyen
Proceedings of COLING 2018, Santa Fe, New-Mexico, August 2018
[PAPER]
- A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing
Minh Nguyen, Toan Nguyen and Thien Huu Nguyen
International Workshop on Security and Privacy Analytics - Anti Phishing Shared Task (IWSPA-AP), March 2018
[PAPER]
- Graph Convolutional Networks with Argument-Aware Pooling for Event Detection
Thien Huu Nguyen and Ralph Grishman
Proceedings of AAAI 2018, New Orleans, Lousiana, February 2018
[PAPER]
- Deep Learning for Information Extraction
Thien Huu Nguyen
Ph.D. Thesis, New York University, January 2018.
[PAPER]
2017
- Domain Adaptation for Relation Extraction with Domain Adversarial
Lisheng Fu, Thien Huu Nguyen, Bonan Min and Ralph Grishman
Proceedings of IJCNLP 2017, Taipei, Taiwan, November 2017
[PAPER]
2016
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New York University 2016 System for KBP Event Nugget: A Deep Learning Approach
Thien Huu Nguyen, Adam Meyers and Ralph Grishman
Proceedings of Text Analysis Conference (TAC), Gaithersburg, Maryland, USA, November 2016
[PAPER]
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Joint Learning of Local and Global Features for Entity Linking via Neural Networks
Thien Huu Nguyen, Nicolas Fauceglia, Mariano Rodriguez Muro, Oktie Hassanzadeh, Alfio Massimiliano Gliozzo and Mohammad Sadoghi
Proceedings of COLING 2016, Osaka, Japan, December 2016
[PAPER]
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Modeling Skip-Grams for Event Detection with Convolutional Neural Networks
Thien Huu Nguyen and Ralph Grishman
Proceedings of EMNLP 2016, Austin, Texas, November 2016
[PAPER]
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Joint Event Extraction via Recurrent Neural Networks
Thien Huu Nguyen, Kyunghyun Cho and Ralph Grishman
Proceedings of NAACL 2016, San Diego, June 2016
[PAPER][CODE]
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A Two-stage Approach for Extending Event Detection to New Types via Neural Networks
Thien Huu Nguyen, Lisheng Fu, Kyunghyun Cho and Ralph Grishman
Proceedings of ACL Workshop on Representation Learning for NLP, Berlin, Germany, 2016
[PAPER]
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Combining Neural Networks and Log-linear Models to Improve Relation Extraction
Thien Huu Nguyen and Ralph Grishman
Proceedings of IJCAI Workshop on Deep Learning for Artificial Intelligence, New York, USA, 2016
[PAPER][CODE]
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Toward Mention Detection Robustness with Recurrent Neural Networks
Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, and Radu Florian
Proceedings of IJCAI Workshop on Deep Learning for Artificial Intelligence, New York, USA, 2016
[PAPER]
2015
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Improving Event Detection with Abstract Meaning Representation
Xiang Li, Thien Huu Nguyen, Kai Cao, and Ralph Grishman
Proceedings of ACL-IJCNLP Workshop on Computing News Storylines (CNewS 2015), Beijing, China, July, 2015
[PAPER]
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Event Detection and Domain Adaptation with Convolutional Neural Networks
Thien Huu Nguyen and Ralph Grishman
Proceedings of ACL-IJCNLP 2015, Beijing, China, July, 2015
[PAPER]
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Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction
Thien Huu Nguyen, Barbara Plank and Ralph Grishman
Proceedings of ACL-IJCNLP 2015, Beijing, China, July, 2015
[PAPER][CODE]
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Relation Extraction: Perspective from Convolutional Neural Networks
Thien Huu Nguyen and Ralph Grishman
Proceedings of NAACL Workshop on Vector Space Modeling for NLP, Denver, Colorado, June, 2015
[PAPER][CODE]
2014
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New York University 2014 Knowledge Base Population Systems
Thien Huu Nguyen, Yifan He, Maria Pershina, Xiang Li, and Ralph Grishman
Proceedings of Text Analysis Conference (TAC), Gaithersburg, Maryland, USA, November 2014
[PAPER]
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Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction
Thien Huu Nguyen and Ralph Grishman
Proceedings of ACL 2014, Baltimore, Maryland, USA, June 2014
[PAPER]
2013 and before
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Combining Proper Name-Coreference with Conditional Random Fields for Semi-supervised Named Entity Recognition in Vietnamese Text
Rathany Chan Sam, Huong Thanh Le, Thuy Thanh Nguyen, and Thien Huu Nguyen
Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Shenzhen, China, May, 2011
[PAPER]
TEACHING
- CIS 313 - Intermediate Data Structures (W19)
- CIS 607 - Seminar on Deep Learning for Natural Language Processing(W19)
PROFESSIONAL SERVICES
- Reviewer: Neural Computation Journal, Transactions on Asian and Low-Resource Language Information Processing, Computational Linguistics
- Program Committee: NAACL (2016, 2018, 2019), COLING (2016, 2018), ACL (2017, 2018), EMNLP (2017, 2018), IJCAI (2017), LREC (2018), Rep4NLP (2017, 2018)
AWARDS
- IBM Ph.D. Fellowship, 2016
- Dean's Dissertation Fellowship, Graduate School of Arts and Science, NYU, 2016-2017
- Harold Grad Prize, Courant Institute of Mathematical Science, NYU, 2016
- Henry MacCracken Fellowship, New York University, 2012 - 2017
- Second Prize in Student Scientific Research Conference, by Ministry of Education and Training, Vietnam, 2012
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