A Brief Summary of Research Interests
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Abstract
I am a former graduate student of
Michael Collins
at
MIT CSAIL.
In previous work, I have received a B.S., M.Eng, and Ph.D
in
EECS at MIT.
My research interests include structured classification,
discriminative methods, and semi-supervised learning.
1 Introduction
I am an alumnus of
MIT CSAIL,
advised by
Michael Collins.
I am interested in discriminative modeling for structured
classification and, more recently, in semi-supervised
learning methods for NLP.
2 Dependency parsing
My research has focused on dependency parsing, a syntactic
formalism whose grounded, lexicalized nature makes it an
attractive target for feature-rich discriminative
models. At the same time, dependency parsers are able to
recover core head-modifier relationships.
3 Structured linear models
Structured linear models are a discriminative
classification framework composed of three components: (1)
a factorization, which specifies a decomposition of
structured labels into sets of parts, allowing efficient
decoding and inference; (2) a feature mapping,
which represents each part as a vector of
real-valued features; and (3) a parameter
estimation algorithm, which learns a weighting for
each feature.
Each component can be improved in a modular fashion. For
example, the factorization can be improved by using a
second- or third-order parser instead of a
first-order parser, features can be improved by
adding cluster based information, and parameter estimation
can be adjusted by selecting between the
structured perceptron,
max-margin models,
or
log-linear models.
4 Future work
I am currently at
Google Research
under the auspices of
Fernando Pereira.
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References
T. Koo,
A.M. Rush,
M. Collins,
T. Jaakkola, and
D. Sontag.
2010.
Dual
Decomposition for Parsing with Non-Projective Head
Automata.
In Proceedings of EMNLP, pages
1288–1298.
[Best paper award].
[slides]
T. Koo.
Advances in Discriminative
Dependency Parsing 2010.
PhD thesis, MIT, June 2010.
[4up]
[slides]
T. Koo and
M. Collins.
2010.
Efficient Third-order
Dependency Parsers.
In Proceedings of ACL, pages 1–11.
[slides]
[code]
X. Carreras,
M. Collins, and
T. Koo.
2008.
TAG, Dynamic
Programming, and the Perceptron for Efficient,
Feature-rich Parsing.
In Proceedings of CoNLL, pages 9–16.
[Best paper award].
T. Koo,
X. Carreras, and
M. Collins.
2008.
Simple
Semi-supervised Dependency Parsing.
In Proceedings of ACL, pages 595–603.
[clusters]
M. Collins,
A. Globerson,
T. Koo,
X. Carreras, and
P. Bartlett.
Exponentiated Gradient
Algorithms for Conditional Random Fields and
Max-Margin Markov Networks.
Journal of Machine Learning Research
9(Aug):1775–1822, 2008.
[src]
T. Koo,
A. Globerson,
X. Carreras, and
M. Collins.
2007.
Structured Prediction
Models via the Matrix-Tree Theorem.
In Proceedings of EMNLP, pages 141–150.
[src]
A. Globerson,
T. Koo,
X. Carreras, and
M. Collins.
2007.
Exponentiated
Gradient Algorithms for Log-Linear Structured
Prediction.
In Proceedings of ICML, pages 305–312.
T. Koo and
M. Collins.
2005.
Hidden-Variable
Models for Discriminative Reranking.
In Proceedings of EMNLP, pages 507–514.
M. Collins and
T. Koo.
Discriminative Reranking
for Natural Language Parsing.
Computational Linguistics
31(1):25–69, 2005.
T. Koo and
M. Collins.
2004.
Parse
Reranking with WordNet Using a Hidden-Variable
Model.
M.Eng Thesis, Massachusetts Institute of Technology,
Cambrige, MA, USA.
J. Hajič,
M. Čmejrek,
B. Dorr,
Y. Ding,
J. Eisner,
D. Gildea,
T. Koo,
K. Parton,
G. Penn,
D. Radev, and
O. Rambow.
2002.
Natural
Language Generation in the Context hyphenate of Machine
Translation; Section 3.3, Chapter 6 (with Jan
Hajič).
NLP WS-02 Final Report.
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