719 Broadway, Room New York, New York 10003, United States Homepage: thien/

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1 THIEN HUU NGUYEN Contact Information Objective Research Interests Education 719 Broadway, Room New York, New York 10003, United States Homepage: thien/ To pursue a research-oriented career in Computer Science Information Extraction, Natural Language Processing, Domain Adaptation, Deep Learning, Machine Learning New York University (NYU) Courant Institute of Mathematical Sciences Department of Computer Science PhD Candidate, expected May 2017 M.S., Computer Science, May 2014 (GPA: 3.889) Hanoi University of Science and Technology (HUST), Hanoi,Vietnam B.S., Computer Science, July 2011 Honor Program, Center for Training of Excellent Students Graduate Grade: Very good GPA: 8.61/10 (top 3% of 600 graduated students) Thesis: Extracting named entities from Vietnamese documents: a semi-supervised approach Professional Experience Information Extraction Research Intern at the IBM T.J. Watson Research Center, Yorktown Heights, New York, USA. I propose a Bidirectional Recurrent Neural Network (BRNN) to improve the robustness for Mention Detection. My BRNN system not only outperforms the best reported system (up to 9% relative error reduction) but also achieves the state-of-the-art performance in domain shifts for English mention detection. In addition, I significantly improve the state-of-theart named entity recognition performance for Dutch (up to 28% relative error reduction). (this work is published at IJCAI 2016) June Aug 2015 I propose a framework to jointly learn local and global features for entity linking based on convolutional and recurrent neural networks. The system achieves the state-of-the-art performance for entity linking on both the general and the domain adaptation settings. In addition, I am involved in developing an unsupervised system for entity disambiguation that can link entity mentions to nodes of any knowledge graphs. I learn embeddings for entity (nodes) in the knowledge graphs that are then used to build the collective disambiguation graph. (this work is published at COLING 2016). June Aug 2016 Deep Learning for Information Extraction: to apply deep learning to information extraction. (NYU, Sep 2014 present) Designing a Convolutional Neural Network for relation extraction which avoids any feature engineering but still achieves the state-of-the-art performance on relation classification and good performance on relation extraction (this work is published at NAACL 2015). Applying Convolutional Neural Networks with word embeddings for trigger detection and domain adaptation in event extraction (achieved the state-of-the-art performance for event detection in both the general setting and the domain adaptation setting) (this work is published at ACL-IJCNLP 2015).

2 Design a joint framework for Event Extraction (i.e, jointly labeling triggers and argument roles for events) based on Bidirectional Recurrent Neural Networks with memory vectors/matrices (achieved the state-of-the-art performance for event extraction on the ACE 2005 dataset) (this work is published at NAACL 2016). Knowledge Base Population (KBP) (by NIST): the challenge is to automatically find information pieces in a large corpus to fill in attribute slots for the entities of interest. (NYU, Jun 2014 present) Key member for the NYU 2014 KBP system for Slot Filling: introducing two distant supervision modules into the system: i) the MaxEnt-based distant supervision module (trained on the data generated by the alignment of Freebase tuples and large corpus), ii) the Multi-Instance Multi-Label (MIML) distant supervision module with guidance (trained on the data produced by the alignment of the Wikipedia Info box and Wikipedia articles) Key member for the NYU 2014 KBP system for Cold Start: integrating one distant supervision module and one inference module (allowing the system to infer more relation assertions based on the existing assertions in the system) (ranked 2nd place among the participating systems) Domain Adaptation for Information Extraction (in the IARPA s project Knowledge Discovery and Dissemination (KDD) ): the motivation is to adapt the models trained on the source domains with many labeled data so that the adapted models can work well on the target domains without or with very little training data. This is very useful in reality as we often want to extend our work to new domains where labeled data is not available yet. (NYU, December 2012 present) Conducting experiments on applying Word Clusters (generated from a large scale unlabeled corpus) to enhance the Feature Augmentation technique in building adaptive name taggers Analyzing the features for Relation Extraction to distinguish between domain-specific and domain-independent features Experimenting on Instance Weighting methods for the Covariate Shift problem of Relation Extraction using Tree Kernels: Kernel Mean Matching (KMM), Kullback-Leibler Importance Estimation Procedure (KLIEP) Employing word representations (Word Clusters, Word Embeddings) for Domain Adaptation of Relation Extraction (achieved up to 7% relative improvement over the best reported system) (this work is published in ACL 2014 and ACL-IJCNLP 2015). Research on semi-supervised algorithms for Vietnamese Named Entity Recognition, namely: (HUST, June 2010 July 2011) Combining name variation heuristics with a confidence estimator based on Conditional Random Fields to bootstrap statistical models for extracting named entities of Vietnamese language (this work was published in PAKDD 2011). Applying Semi-supervised Conditional Random Fields for named entity recognition of Vietnamese language Develop an Inductive Logic Programming method to automatically construct extraction rules for the Information Extraction problem of Vietnamese language (HUST, October 2009 May 2010) (this work was published in SoICT 2010). Develop a Relation Extraction system for Vietnamese language using Support Vector Machines (HUST, December 2010 May 2011)

3 Design an information extraction system based on various machine learning technique to construct knowledge bases from web pages of Vietnamese scientists (HUST, December 2010 May 2011) Data mining and Machine Learning Explore agglomerative clustering schemes to learn Fuzzy Concept Hierarchy from databases automatically (HUST, February 2009 October 2009) Construct a Profile Spammer Detection System for the Zingme social network (the largest social network in Vietnam) (R&D Lab, VNG Corp, Vietnam, September 2011 Jun 2012) Research on the Behavioral Targeting problem focusing on two main tasks: User Behavior Segmentation and User Segment Ranking (R&D Lab, VNG Corp, Vietnam, September 2011 Jun 2012) Honours and Awards Publications IBM Ph.D. Fellowship, Dean s Dissertation Fellowship, Graduate School of Arts and Science, NYU, Harold Grad Prize, Courant Institute of Mathematical Science, NYU, nd in KBP Cold Start, TAC 2014 Henry MacCracken Fellowship, New York University, Vietnam Education Foundation (VEF) Fellowship, (recommended by US National Academy) Second Prize in Student Scientific Research Conference, by Ministry of Education and Training, Vietnam, 2012 First Prize in Student Scientific Research Conference, by HUST, June, 2011 Merit Certificate for Excellent Students, by HUST, July, 2011 Annual Ministry of Educational and Training Scholarship for Excellent Students, Vietnam, Second Prize in the National Mathematical Competition for High School Students, by Ministry of Education and Training, Vietnam, 2006 First Prize in the Mathematical Competition of Hung Yen Province, Vietnam 2006 Incentive Award in the National Mathematical Competition for High School Students, by Ministry of Education and Training, Vietnam, 2005 Thien Huu Nguyen, Nicolas Fauceglia, Mariano Rodriguez Muro, Oktie Hassanzadeh, Alfio Massimiliano Gliozzo and Mohammad Sadoghi, Joint Learning of Local and Global Features for Entity Linking via Neural Networks, in Proceedings of COLING 2016, Osaka, Japan, December, Thien Huu Nguyen and Ralph Grishman, Modeling Skip-Grams for Event Detection with Convolutional Neural Networks, in Proceedings of EMNLP 2016, Austin, Texas, USA, November, Thien Huu Nguyen, Kyunghyun Cho and Ralph Grishman, Joint Event Extraction via Recurrent Neural Networks, in Proceedings of NAACL 2016, San Diego, USA, June, Thien Huu Nguyen, Lisheng Fu, Kyunghyun Cho and Ralph Grishman, A Two-stage Approach for Extending Event Detection to New Types via Neural Networks, in Proceedings of ACL Workshop on Representation Learning for NLP (RepL4NLP), Berlin, Germany, August, Thien Huu Nguyen and Ralph Grishman, Combining Neural Networks and Log-linear Models to Improve Relation Extraction, in Proceedings of IJCAI Workshop on Deep Learning for Artificial Intelligence (DLAI), New York, USA, July, Thien Huu Nguyen, Avirup Sil, Georgiana Dinu and Radu Florian, Toward Mention Detection Robustness with Recurrent Neural Networks, in Proceedings of IJCAI Workshop on Deep Learning for Artificial Intelligence (DLAI), New York, USA, July, 2016.

4 Xiang Li, Thien Huu Nguyen, Kai Cao and Ralph Grishman, Improving Event Detection with Abstract Meaning Representation, in Proceedings of ACL-IJCNLP Workshop on Computing News Storylines (CNewS 2015), Beijing, China, July, Thien Huu Nguyen and Ralph Grishman, Event Detection and Domain Adaptation with Convolutional Neural Networks, in Proceedings of ACL-IJCNLP 2015, Beijing, China, July, Thien Huu Nguyen, Barbara Plank and Ralph Grishman, Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction, in Proceedings of ACL-IJCNLP 2015, Beijing, China, July, Thien Huu Nguyen and Ralph Grishman, Relation Extraction: Perspective from Convolutional Neural Networks, in Proceedings of NAACL Workshop on Vector Space Modeling (VSM) for NLP, Denver, Colorado, June, Thien Huu Nguyen, Yifan He, Maria Pershina, Xiang Li and Ralph Grishman, New York University 2014 Knowledge Base Population Systems, in Proceedings of Text Analysis Conference (TAC), Gaithersburg, Maryland, USA, November Thien Huu Nguyen and Ralph Grishman, Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction, in Proceedings of ACL 2014, pp 68-74, Baltimore, Maryland, USA, June Thien Huu Nguyen, Vinh Quang Nguyen, and Ngoc Minh Thi Nguyen, An information extraction system for constructing knowledge bases from Vietnamese documents, in Proceedings of the 28th Student Scientific Research Conference, pp , School of Information and Communication Technology, HUST, Hanoi, Vietnam, May, Rathany Chan Sam, Huong Thanh Le, Thuy Thanh Nguyen, and Thien Huu Nguyen, Combining Proper Name-Coreference with Conditional Random Fields for Semi-supervised Named Entity Recognition in Vietnamese Text, in Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp , Shenzhen, China, May, Huong Thanh Le and Thien Huu Nguyen, Named Entity Recognition using Inductive Logic Programming, in Proceedings of the Symposium on Information and Communication Technology, Hanoi University of Science and Technology (SoICT), pp 71-78, Hanoi, Vietnam, August, Technical Skills Programming Languages Java, Python, Shell (use everyday) C/C++, AWK, MatLab, L A TEX(use when necessary) Others: Mallet, Lucene, MySQL, theano Teaching Experience New York University, New York Teaching Assistant for CSCI-GA.2590: Natural Language Processing Spring 2015 Graduate level course in Natural Language Processing Professor: Prof. Ralph Grishman Professional Service Reviewer Neural Computation Journal Program Committee NAACL 2016, COLING 2016

5 Referees Ralph Grishman, PhD Professor, Computer Science Department of Computer Science Courant Institute of Mathematical Sciences New York University Kyunghyun Cho, PhD Professor, Computer Science Department of Computer Science, Courant Institute of Mathematical Sciences Center for Data Science New York University