UTCS AI Colloquia - Percy Liang, Stanford University, "Learning Latent-Variable Models of Language"
Signup Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi
Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties
Host: Ray Mooney
Talk Abstract: Effective information extraction and question answering require modeling of the deep syntactic and semantic structures of natural language. At the same time, acquiring training data specifying these full structures is prohibitively expensive. Our goal is therefore to learn models of language that can induce latent structures (e.g., parse trees) from raw observations (e.g., sentences) in an unsupervised way. First, I will present spectral methods for learning latent parse tree models. In contrast to existing algorithms for learning latent-variable models such as EM, our method has global convergence guarantees. Second, I will present a semantic model that maps natural language questions to answers via a latent database query, and show results on large-scale question answering.
Speaker Bio: Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004 Ph.D. from UC Berkeley, 2011). His research focuses on methods for learning richly-structured statistical models from limited supervision, most recently in the context of semantic parsing in natural language processing. He won a best student paper at the International Conference on Machine Learning in 2008, received the NSF, GAANN, and NDSEG fellowships, and is also a 2010 Siebel Scholar.
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