Deep Learning for Natural Language Processing and Beyond
The Tutorial begins with the basic of feed-forward neural network and relevant fundamental knowledge for deep learning. We then introduce more specialized neural network models, including Convolutional Neural Network, Recurrent Neural Network, and attention-based models. In the second part, we will present how these models and techniques can be applied to some interesting problems of natural language processing including sentiment classification, textual entailment recognition, natural language generation, and question answering. The last part of the tutorial will show how we can adapt deep learning and natural language processing techniques for program analysis.
Assoc. Prof. Nguyen Le Minh – Japan Advanced Institute for Science and Technology (Japan)
Minh Le Nguyen is currently an Associate Professor of School of Information Science, JAIST. He leads the lab on Machine Learning and Natural language Understanding at JAIST. He received his B.Sc. degree in information technology from Hanoi University of Science, and M.Sc. degree in information technology from Vietnam National University, Hanoi in 1998 and 2001, respectively. He received his Ph.D. degree in Information Science from School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) in 2004. He was an assistant professor at School of information science, JAIST from 2008-2013. His research interests include machine learning, natural language understanding, question answering, text summarization, machine translation, big data mining, and Deep Learning.