摘要：In this talk I will discuss the machine learning foundation behind natural language understanding and a wide range of empirical tasks thereupon, such as topic/sentiment understanding, language modeling, name entity recognition, parsing, machine translation, and question answering. These tasks represent an ordering of problems with increasing degree of "difficulty" and "humanity" in terms of development from unintelligence "mechanical text digestion" to intelligence "human-like reading-comprehension". I will discuss the mathematical and algorithmic basis of machine learning methods that enable computational competence over such tasks, the connections between these methods and these tasks, and thoughts on future directions.
简介：Dr. Eric Xing is a professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in complex systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. Professor Xing has published over 200 peer-reviewed papers, and is an associate editor of the Journal of the American Statistical Association, Annals of Applied Statistics, the IEEE Transactions of Pattern Analysis and Machine Intelligence, the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning journal, and the Journal of Machine Learning Research. He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Alfred P. Sloan Research Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Faculty Award.