STRUCTURE AND KNOWLEDGE IN NATURAL LANGUAGE PROCESSING HAL DAUME III Department of Computer Science, University of Maryland Human language exhibits complex structure. To be successful, machine learning approaches to language-related problems must be able to take advantage of this structure. I will discuss several investigations into the relationship between structure and learning, which have led to some surprising conclusions about the role that structure plays in language processing. I will describe some recent efforts related to learning strategies that not only aim to do a good job, but aim to do it quickly. From there, I will consider the question of: where does this structure come from. By taking insights from linguistic typology, I will show that very simple typological information can lead to significant increases in system performance for some simple syntactic problems. Moreover, I will show how this typological information can be mined from raw data. (This talk includes joint work with Dan Klein, John Langford, Percy Liang, Daniel Marcu, and some of my students: Arvind Agarwal, Adam Teichert, and Piyush Rai.) BIO Hal Daumé III is an assistant professor in Computer Science at the University of Maryland, College Park. He holds joint appointments in UMIACS and Linguistics. His primary research interest is in understanding how to get human knowledge into a machine learning system in the most efficient way possible. He works primarily in the areas of language (computational linguistics and natural language processing) and machine learning (structured prediction, domain adaptation, and Bayesian inference). He associates himself most with conferences like ACL, ICML, NIPS, and EMNLP, and has over 30 conference papers (one best paper award in ECML/PKDD 2010) and 7 journal papers. He earned his PhD at the University of Southern California with a thesis on structured prediction for language (his advisor was Daniel Marcu). He spent the summer of 2003 working with Eric Brill in the machine learning and applied statistics group at Microsoft Research. Prior to that, he studied math (mostly logic) at Carnegie Mellon University. He still likes math and does not like to use C (instead he uses O'Caml or Haskell). He does not like shoes, but does like activities that are hard on your feet: skiing, badminton, Aikido, and rock climbing.