Tutorial: Methods for Deep Natural Language Processing    

Professor Dragomir Radev

Tutorial Summary: A large number of Natural Language Processing (NLP) problems, including parsing, machine translation, paraphrasing, sequence modeling, question answering, semantic analysis, and dialogue modeling have in recent years been tackled with Deep Neural Network (DNN) methods. I will present (1) an overview of the fundamentals of NLP (including ambiguity resolution, parsing, vector semantics), (2) learning methods (linear classifiers, the perceptron, multi-layed neural     networks), and (3) some recent advancements in DNN for NLP.    

Bio: Dragomir Radev is a Professor of Computer Science at Yale University and faculty-in-residence at the Yale Institute for Network Science. He holds a PhD in Computer Science from Columbia University. He has previously taught at Columbia University and the University of Michigan. He has also worked for or consulted for a large number of technology companies. Dragomir’s recent research is on Natural Language Processing, Machine Learning, and Information Retrieval. He has more than 200 research publications and three patents. Dragomir is a Fellow of the Association for Computer Machinery (ACM).

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