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Colloquium - Allen

Deep Natural Language Understanding
University of Rochester

Deep language understanding involves mapping language to its intended meaning in context, using concepts and relations in an ontology that supports knowledge and reasoning. Currently, one would think there was consensus across the field of computational linguistics that deep understanding is not possible, and almost all current research in the field focuses on developing new machine learning techniques over large corpora. I will argue that while this research is producing significant results, and will continue to do so, it has also served to isolate the field from its original home within Artificial Intelligence. As a result, current natural language work is almost completely divorced from work in reasoning, planning and acting. In this talk I will argue that, contrary to current thought, an effective level of deep understanding is a very viable research area. I will present examples of recent work to support these claims. Interestingly, while I argue that the current statistical paradigm is unlikely to achieve deep understanding, it is also the case that deep understanding will likely only be possible by exploiting the advances in statistical approaches.

James Allen is the John H. Dessauer Professor of Computer Science at the University of Rochester, as well as Associate Director of the Institute for Human and Machine Cognition in Pensacola, Florida. He received his PhD in Computer Science from the University of Toronto and was a recipient of the Presidential Young Investigator award from NSF in 1984. A Founding Fellow of the American Association for Artificial Intelligence (AAAI), he was editor-in-chief of the journal Computational Linguistics from 1983-1993. He has authored numerous research papers in the areas natural language understanding, knowledge representation and reasoning, and spoken dialogue systems.

Department of Computer Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
May 5, 2012 (14:13)