Paul G. Allen Center, Microsoft Atrium
Noah Smith, Associate Professor
UW Computer Science & Engineering
Discovering the Social World using Text
Abstract
Research in natural language processing is increasingly taking into account the context -- physical and social -- in which linguistic communication occurs. In this talk, I will present examples of models of text data that exploit assumptions about the social world from which those data emerged to further our understanding. First, we'll look at how city-scale linguistic groups in the US influence each other, using a large collection of social media messages. Second, we'll turn to models of how a very small, powerful set of individuals -- the Supreme Court justices -- are influenced by highly-incentivized generators of text.
Speaker Bio
Noah Smith is Associate Professor of Computer Science & Engineering at the University of Washington. Previously, he was on the faculty of the Language Technologies and Machine Learning Department in the School of Computer Science at Carnegie Mellon University (2006–2015). His research tackles many problems in natural language processing, machine learning, and their applications, especially to the social sciences. His work and that of his research group, Noah's ARK, has been recognized with a Finmeccanica career development chair at CMU (2011–2014), an NSF CAREER award (2011–2016), a Hertz Foundation graduate fellowship at Johns Hopkins University (2001–2006), numerous best paper nominations and awards, and coverage by NPR, BBC, CBC, the New York Times, Washington Post, and Time.