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

[Dana Houston]

> INTELLIGENCE SEMINAR
> MARCH 29 AT 1:30pm, IN GHC 4405
> (PLEASE NOTE THE UNUSUAL ROOM AND TIME)
>
> SPEAKER: HAL DAUME III (University of Maryland)
> Host: Noah Smith
> For meetings, contact Stacey Young (staceyy at cs.cmu.edu)
>
> STRUCTURE AND KNOWLEDGE IN NATURAL LANGUAGE PROCESSING
>
> 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 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