How can we use text to tell us what is happening in the real world? Text-driven forecasting is the challenge of making concrete, testable predictions about future events and trends from publicly available text data. Text-based modeling methods make it possible to discover the agendas and attitudes behind the words people use. In this panel, we consider some recent success stories that use various kinds of text (expert-written analysis, blog posts, tweets) to tell us interesting things about the future and about the people behind the texts in various domains (finance, political discourse, and public opinion polls).
| Noah A. Smith | Philip Resnik |
| Assistant Professor | Professor |
| Carnegie Mellon University | University of Maryland |
| Noah Smith is an assistant professor in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science, as a Hertz Foundation Fellow, 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 for structured data, and applications of natural language processing. He serves on the editorial board of the journal Computational Linguistics and received a best paper award at the ACL 2009 conference. His research group, Noah's ARK, is supported by the NSF (including an NSF CAREER award), DARPA, Qatar NRF, IARPA, Portugal FCT, and gifts from Google, HP Labs, IBM Research, and Yahoo Research. | Philip Resnik is a professor at the University of Maryland, holding joint appointments in the Department of Linguistics and at the Institute for Advanced Computer Studies. He received his bachelor's degree in Computer Science at Harvard in 1987, and his Ph.D. in Computer and Information Science at the University of Pennsylvania in 1993, and he has worked in industry R&D at Bolt Beranek and Newman, IBM T.J. Watson Research Center, and Sun Microsystems Laboratories. Dr. Resnik's research focuses on combining knowledge-based and statistical methods for natural language processing, with applications in machine translation, translation crowdsourcing, and computational social science. His current work is supported by NSF, DARPA, IARPA, ARL, and a Google Research Award. Outside academia, he serves as strategic technology advisor for CodeRyte Inc., the nation's fastest growing provider of NLP solutions in healthcare, and as lead scientist for Converseon, a leading social media consultancy. |