[Intelligence Seminar] Talk of interest: Friday, Oct. 2, 10:30: "Constrained conditional models: learning and inference in natural language processing, " University of Pittsburgh

[Noah A Smith]

University of Pittsburgh Department of Computer Science Distinguished
Lecturer Series

CONSTRAINED CONDITIONAL MODELS: LEARNING AND INFERENCE IN NATURAL
LANGUAGE UNDERSTANDING

DAN ROTH

Professor, Department of Computer Science, University of Illinois at
Urbana-Champaign

Friday October 2, 2009	
10:30 am	 - SENSQ 5317

Hosted by Diane J. Litman

ABSTRACT

Making decisions in natural language understanding tasks often
involves assigning values to sets of interdependent variables where an
expressive dependency structure among these can influence, or even
dictate, what assignments are possible. Structured learning problems
provide one such example, but we are interested in a broader setting
where multiple models are involved, global inference is over these is
essential, but it may not be ideal, or possible, to learn them
jointly.

I will present work on Constrained Conditional Models (CCMs), a
framework that augments probabilistic models with declarative
constraints as a way to support decisions in an expressive output
space while maintaining modularity and tractability of training. The
focus will be on discussing training and inference paradigms for
Constrained Conditional Models, with examples drawn from natural
language understanding tasks such as semantic role learning,
information extraction tasks, and transliteration.
BIOGRAPHY OF SPEAKER

Dan Roth is a Professor in the Department of Computer Science and the
Beckman Institute at the University of Illinois at Urbana-Champaign
and a Willet Faculty Scholar of the College of Engineering. He is the
director of a DHS Center for Multimodal Information Access & Synthesis
(MIAS) and has faculty positions also at the Statistics and
Linguistics Departments.

Roth is a fellow of AAAI and has published broadly in machine
learning, natural language processing, knowledge representation and
reasoning and learning theory, and has developed advanced machine
learning based tools for natural language applications that are being
used widely by the research community, including an award winning
Semantic Parser.

Prof. Roth has given keynote talks in major conferences, including
AAAI, The Conference of the American Association Artificial
Intelligence; ICMLA, The International Conference on Machine Learning
and Applications; EMNLP, The Conference on Empirical Methods in
Natural Language Processing, and ECML & PKDD, the European Conference
on Machine Learning and the Principles and Practice of Knowledge
Discovery in Databases. He has also presented several tutorials in
universities and conferences including at ACL and the European ACL.
Among his paper awards are the best paper award in IJCAI-99 and the
2001 AAAI Innovative Applications of AI Award. Roth was the program
chair of CoNLL'02 and of ACL'03, and is or has been on the editorial
board of several journals in his research areas; he is currently an
associate editor for the Journal of Artificial Intelligence Research
and the Machine Learning Journal.

Prof. Roth got his B.A Summa cum laude in Mathematics from the
Technion, Israel, and his Ph.D in Computer Science from Harvard
University in 1995.