Learning and Inference for Natural Language Understanding

时间:10月9日上午9:30-11:00

地点:计算机所楼106会议室

题目:Learning and Inference for Natural Language Understanding

摘要:Machine Learning and Inference methods have become ubiquitous and have had a broad impact on a range of scientific advances and technologies and on our ability to make sense of large amounts of data. Research in Natural Language Processing has both benefited from and contributed to advancements in these methods and provides an excellent example for some of the challenges we face moving forward.

I will describe some of our research in developing learning and inference methods in pursue of natural language understanding. In particular, I will address what I view as some of the key challenges, including (i) learning models from natural interactions, without direct supervision, (ii) knowledge acquisition and the development of inference models capable of incorporating knowledge and reason, and (iii) scalability and adaptation—learning to accelerate inference during the life time of a learning system.

A lot of this work is done within the unified computational framework of Constrained Conditional Models (CCMs), an Integer Linear Programming formulation that augments statistically learned models with declarative constraints as a way to support learning and reasoning. Within this framework, I will discuss old and new results pertaining to learning and inference and how they are used to push forward our ability to develop programs that understand natural language.

报告人:Prof. Dan Roth

(Computer Science and the Beckman Institute, University of Illinois at Urbana-Champaign)

Dan Roth is a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana-Champaign and a University of Illinois Scholar. He received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D. in Computer Science from Harvard University in 1995.

Roth is a Fellow of the American Association for the Advancement of Science (AAAS), the Association of Computing Machinery (ACM), the Association for the Advancement of Artificial Intelligence (AAAI), and the Association of Computational Linguistics (ACL), for his contributions to Machine Learning and to Natural Language Processing.

He has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory, and has developed advanced machine learning based tools for natural language applications that are being used widely by the research community and commercially.

Roth is the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). He was the program chair of AAAI’11, ACL’03 and CoNLL'02.

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