[Intelligence Seminar] March 29, 1:30pm: Presentation by Hal Daume

Dana Houston dhouston at cs.cmu.edu
Fri Mar 18 09:32:02 EDT 2011

MARCH 29 AT 1:30pm, IN GHC 4405

SPEAKER: HAL DAUME III (University of Maryland)
Host: Noah Smith
For meetings, contact Stacey Young (staceyy at cs.cmu.edu)


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.)


Hal Daume 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.

Dana M. Houston
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
5407 Gates Hillman Complex
5000 Forbes Avenue
Pittsburgh, PA 15213

T:  (412)268-6591
F:  (412)268-6298

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