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

Barbara Engelhardt bee at princeton.edu
Mon Feb 29 09:33:07 EST 2016

Talk of interest tomorrow.

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Colloquium Speaker

Greg Durrett, UC Berkeley

Tuesday March 1, 12:30pm

Computer Science 105

riven Text Analysis with Joint Models

 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

Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
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