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My thesis committee is made up of
Daniel Marcu (CS),
Kevin Knight (CS),
Eduard Hovy (CS),
Stefan Schaal (CS),
Gareth James (Statistics),
Andrew McCallum (UMass).
The thesis is available in PDF or
Postscript format (warning: it's a
big file!). BibTeX is also
availble. You can also download my defense slides in either OpenOffice format or PDF (warning: animations don't come
through in PDF).
The thesis abstract is:
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Natural language processing is replete with problems whose outputs are
highly complex and structured. The current state-of-the-art in
machine learning is not yet sufficiently general to be applied to
general problems in NLP. In this thesis, I present Searn (for
"search-learn"), an approach to learning for structured
outputs that is applicable to the wide variety of problems
encountered in natural language (and, hopefully, to problems in other
domains, such as vision and biology). To demonstrate Searn's general applicability,
I present applications in such diverse areas as automatic document
summarization and entity detection and tracking. In these
applications, Searn is
empirically shown to achieve state-of-the-art performance.
Searn is based on an
integration of learning and search. This contrasts with
standard approaches that define a model, learn parameters for that
model, and then use the model and the learned parameters to produce
new outputs. In most NLP problems, the "produce new
outputs" step includes an intractable computation. One must
therefore employ a heuristic search function for the production step.
Instead of shying away from search, Searn attacks it head on and considers structured
prediction to be defined by a search problem. The
corresponding learning problem is then made natural: learn parameters
so that search succeeds.
The two application domains I study most closely in this thesis are
entity detection and tracking (EDT) and automatic document
summarizatio |
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