June 15th, 2008. The Hyatt-Regency. Columbus, OH.
9:00 AM — 12:30 PM
Semi-supervised learning for NLP is a very broad topic, and we realize that we cannot possibly hope to cover all of it in a single tutorial. Because of this, we have provided several supplemental resources pages, each of which includes links to papers on semi-supervised learning for NLP, along with brief descriptions relating them to the topics of the tutorial.
About the Presenters
John Blitzer is a visiting researcher at Microsoft Research Asia. His research area is machine learning for natural language processing, with a primary focus on unsupervised dimensionality reduction of text. He received his Ph.D. from the Univeristy of Pennsylvania, where his advisor was Fernando Pereira. Recently, he has worked on empirical and theoretical analyses of structural learning for semi-supervised domain adaptation. John has published several papers on semi-supervised learning, including applications to tagging, entity recognition, and sentiment classification. He has been a teaching assistant for courses in cognitive science and numerical linear algebra at the University of Pennsylvania.
Xiaojin (Jerry) Zhu is an Assistant Professor in Computer Sciences at University of Wisconsin, Madison. His research interests are statistical machine learning (in particular semi-supervised learning), and its applications to natural language analysis. He received a Ph.D. in Language Technologies from CMU in 2005, with thesis research on graph-based semi-supervised learning. His current research projects aim at bridging the different approaches in semi-supervised learning, and making them more effective for practitioners. He has taught several graduate and undergraduate courses in AI, machine learning and NLP at the University of Wisconsin, Madison.