Hal Daumé III

University of Maryland, College Park

 

Abstract
Many classic problems in natural language processing can be cast as building mapping from a complex input (e.g., a sequence of words) to a complex output (e.g., a syntax tree or semantic graph). This task is challenging both because language is ambiguous (learning difficulties) and represented with discrete combinatorial structures (computational difficulties). Often these are at odds: the features you want to add to decrease learning difficulties cause nontrivial additional structure yielding worse computational difficulties.
I will present approaches to address this problem that explicitly learn to trade-off accuracy and efficiency, applied to a variety of linguistic phenomena. This will include black-box solutions to any problem that can be solved using branch-and-bound (e.g., integer linear programs). Finally, I will show that in some cases, we can actually obtain a model that is faster and more accurate by exploiting smarter learning algorithms.

Bio:
Hal Daumé III is an associate professor in Computer Science at the University of Maryland, College Park. He holds joint appointments in UMIACS and Linguistics. He was previously an assistant professor in the School of Computing at the University of Utah. His primary research interest is in developing new learning algorithms for prototypical problems that arise in the context of language processing and artificial intelligence. This includes topics like structured prediction, domain adaptation and unsupervised learning; as well as multilingual modeling and affect analysis. He associates himself most with conferences like ACL, ICML, NIPS and EMNLP. 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 doesn't like to use C (instead he uses O'Caml or Haskell). He doesn't like shoes, but does like activities that are hard on your feet: skiing, badminton, Aikido and rock climbing.