continuous state machines and grammars for linguistic structure prediction
Linguistic structure prediction infers abstract representations of text, like syntax trees and semantic graphs, enabling interpretation in applications like question answering, information extraction, and opinion analysis. This talk is about the latest family of methods for linguistic structure prediction, which make heavy use of representation learning via neural networks. Dr. Noah Smith presents these new methods as continuous generalizations of state machines and probabilistic grammars. He shows how they've led to fast and accurate performance on several syntactic and semantic parsing problems.
Biography: Noah Smith is an Associate Professor of Computer Science & Engineering at the University of Washington. Previously, he was an Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science from Johns Hopkins University in 2006 and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. His research interests include statistical natural language processing, especially unsupervised methods, machine learning, and applications of natural language processing. His book, Linguistic Structure Prediction, covers many of these topics. He has served on the editorial board of the journals Computational Linguistics (2009–2011), Journal of Artificial Intelligence Research (2011–present), and Transactions of the Association for Computational Linguistics (2012–present), as the secretary-treasurer of SIGDAT (2012–2015), and as program co-chair of ACL 2016. Alumni of his research group, Noah's ARK, are international leaders in NLP in academia and industry. Smith's work has been recognized with a UW Innovation award (2016–2018), a Finmeccanica career development chair at CMU (2011–2014), an NSF CAREER award (2011–2016), a Hertz Foundation graduate fellowship (2001–2006), numerous best paper nominations and awards, and coverage by NPR, BBC, CBC, New York Times, Washington Post, and Time.
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