Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Learning and Inference in Natural Language
Prof. Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
Monday, March 3, 2003, 4:15PM
TCSeq 201
http://robotics.stanford.edu/ba-colloquium/
Abstract
The talk will describe research on learning and
inference in the context of natural language
understanding tasks.
The commonly used machine learning paradigm is concerned
with learning single concepts from examples. In this
framework the learner attempts to learn a single hidden
function, e.g., a sense of a word in a given context,
from a collection of examples. However, in many cases --
as in most natural language situations -- decisions
depend on the outcomes of several different but mutually
dependent classifiers. The classifiers' outcomes need to
respect some constraints that could arise from the
sequential nature of the data or other domain specific
conditions, thus requiring a level of inference on top
the predictions. I will describe a paradigm in which we
address the problem of using the outcomes of several
different classifiers in making coherent inferences --
those that respect constraints on the outcome of the
classifiers, and demonstrate it using several examples
from our work on natural language understanding.
About the Speaker
Dan Roth is an Associate Professor in the Department of
Computer Science at the University of Illinois at
Urbana-Champaign and the Beckman Institute of Advanced
Science and Technology (UIUC). He is a fellow of the
Institute of Advanced Studies at the University of
Illinois and a Willett Faculty Scholar of the College of
Engineering. 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.
His research spans both theoretical work in learning and
intelligent reasoning and work on applying learning and
inference to intelligent human-computer interactions --
focusing on learning and inference for natural languages
understanding related tasks. Among his awards are a best
paper award in the International Joint Conference on
Artificial Intelligence (IJCAI) 1999, an NSF CAREER
Award, the University of Illinois award for research with
undergraduate students and the Xerox Award for Faculty Research.
Currently, he is the program chair of ACL'03, the main
international meeting of the Association for
Computational Linguistics and the natural language
processing community.
Contact: bac-coordinators@cs.stanford.edu
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