[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>
609-258-4562
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