Andrew McCallum

Deep Learning for Representation and Reasoning from Natural Language

In this talk I will describe advances in deep learning for extracting entity-relations from natural language as well as for representing and reasoning about the resulting knowledge base. I will introduce "universal schema," our approach that embeds many database schema and natural language expressions into a common semantic space. Then I will describe recent research in Gaussian embeddings that capture uncertainty and asymmetries, collaborative filtering with text, and logical implicature of new relations through multi-hop relation paths compositionally modeled by recursive neural tensor networks.

Andrew McCallum is a Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He has published over 250 papers in many areas of AI, including natural language processing, machine learning, data mining and reinforcement learning, and his work has received over 45,000 citations. He obtained his PhD from University of Rochester in 1995 with Dana Ballard and a postdoctoral fellowship from CMU with Tom Mitchell and Sebastian Thrun. In the early 2000's he was Vice President of Research and Development at at WhizBang Labs, a 170-person start-up company that used machine learning for information extraction from the Web. He is a AAAI Fellow, the recipient of the UMass Chancellor's Award for Research and Creative Activity, the UMass NSM Distinguished Research Award, the UMass Lilly Teaching Fellowship, and research awards from Google, IBM, Yahoo and Microsoft. He was the General Chair for the International Conference on Machine Learning (ICML) 2012, and is the current president of the International Machine Learning Society, as well as member of the editorial board of the Journal of Machine Learning Research. For the past twenty years, McCallum has been active in research on statistical machine learning applied to text, especially information extraction, entity resolution, semi-supervised learning, topic models, and social network analysis. His work on open peer review can be found at McCallum's web page is

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