[Ml-stat-talks] Fwd: [talks] Colloquium-Greg Durrett, Tuesday, March 1, 12:30PM

Barbara Engelhardt bee at princeton.edu
Tue Feb 23 22:37:03 EST 2016


Talk of interest.

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Colloquium Speaker

Greg Durrett, UC Berkeley

Tuesday, March 1st, 12:30PM

Cs 105





Title: Data-Driven Text Analysis with Joint Models


Abstract: One reason that analyzing text is hard is that it involves
dealing with deeply entangled linguistic variables: objects like syntactic
structures, semantic types, and discourse relations depend on one another
in complex ways.  Our work tackles several facets of text analysis using
joint modeling, combining model components both across and within the
various subtasks of this analysis.  This model structure allows us to pass
information between these entangled subtasks and propagate high-confidence
predictions rather than errors.  Critically, our models have the capacity
to learn key linguistic phenomena as well as other important patterns in
the data; that is, linguistics tells us how to structure these models, then
the data injects knowledge into them.  We describe state-of-the-art systems
for a range of tasks, including syntactic parsing, entity resolution, and
document summarization.


Bio: Greg is a Ph.D. candidate at UC Berkeley working on natural language
processing with Dan Klein.  He is interested in building structured machine
learning models for a wide variety of text analysis problems and downstream
NLP applications.  His work is comprised of two broad thrusts: first,
designing joint models that combine information across different tasks or
different views of a problem, and second, building systems that strike a
balance between being linguistically motivated and data-driven
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