Title: Constrained Conditional Models: Integer Linear Programming
Formulations for Natural Language Understanding

Speaker: Dan Roth
Computer Science and the Beckman Institute
University of Illinois at Urbana/Champaign

Time: 11am-12:30pm, January 31, Thursday

Place: Room 9205/9206, CUNY Graduate Center. 5th Ave & 34th St.


Abstract:

Computational approaches to problems in Natural Language Understanding
and Information Extraction often involve assigning values to sets of
interdependent variables.  Examples include semantic role labeling
(analyzing natural language text at the level of “who did what to whom,
when and where”), syntactic parsing,  Identifying events, entities and
relations in natural language text, transliteration of names, and
textual entailment (determining whether one utterance is a likely
consequence of another).  Over the last few years, one of the most
successful approaches to studying these problems involves Constrained
Conditional Models (CCMs), an Integer Learning Programming formulation
that augments probabilistic models with declarative constraints as a way
to support such decisions.

I will present research within this framework, discussing old and new
results pertaining to inference issues, learning algorithms for training
these global models, and the interaction between learning and inference.


Short Bio:

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 University of Illinois Scholar. He is the director of a DHS Center for
Multimodal Information Access & Synthesis (MIAS) and holds faculty
positions in Statistics, Linguistics and at the School of Library and
Information Sciences.

Roth is a Fellow of the ACM, AAAI and ACL, for his contributions to
Machine Learning and to Natural Language Processing. He 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.

Roth is the Associate Editor-in-Chief of the Journal of Artificial
Intelligence Research (JAIR) and will serve as Editor-in-Chief for a
two-year term beginning in 2015. He was the program chair of AAAI’11,
ACL’03 and CoNLL'02, has been on the editorial board of several
journals in his research areas and has won several teaching and paper
awards. He has also given keynote talks and presented tutorials in some
of the major conferences in his research areas.

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