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EMNLP
2008
13 years 9 months ago
Learning with Probabilistic Features for Improved Pipeline Models
We present a novel learning framework for pipeline models aimed at improving the communication between consecutive stages in a pipeline. Our method exploits the confidence scores ...
Razvan C. Bunescu
NIPS
2008
13 years 9 months ago
Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction
We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free gr...
Shay B. Cohen, Kevin Gimpel, Noah A. Smith
EMNLP
2009
13 years 5 months ago
Polynomial to Linear: Efficient Classification with Conjunctive Features
This paper proposes a method that speeds up a classifier trained with many conjunctive features: combinations of (primitive) features. The key idea is to precompute as partial res...
Naoki Yoshinaga, Masaru Kitsuregawa
COLING
2010
13 years 2 months ago
Kernel Slicing: Scalable Online Training with Conjunctive Features
This paper proposes an efficient online method that trains a classifier with many conjunctive features. We employ kernel computation called kernel slicing, which explicitly consid...
Naoki Yoshinaga, Masaru Kitsuregawa
EMNLP
2011
12 years 7 months ago
Dual Decomposition with Many Overlapping Components
Dual decomposition has been recently proposed as a way of combining complementary models, with a boost in predictive power. However, in cases where lightweight decompositions are ...
André L. Martins, Noah A. Smith, Már...