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

Mitra D. Kelly mkelly at CS.Princeton.EDU
Tue Feb 23 13:59:39 EST 2016

Colloquium Speaker

Greg Durrett, UC Berkeley

Tuesday, March 1st, 12:30PM

Cs 105



Data-Driven 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


Mitra Kelly

Academic Secretary

Princeton University

Computer Science Dept

35 Olden Street

Princeton NJ 08540

mkelly at cs.princeton.edu <mailto:mkelly at cs.princeton.edu> 



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