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2010
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High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models

9 years 3 months ago
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation constraints estimated with labeled data. This approach encourages the generative model to discover latent structure that is relevant to a prediction task. We estimate parameters with a coordinate ascent algorithm, one step of which involves training a discriminative log-linear model with an embedded generative model. This hybrid model can be used for test time prediction. Unlike other high-performance semi-supervised methods, the proposed algorithm converges to a stationary point of a single objective function, and affords additional flexibility, for example to use different latent and output spaces. We conduct experiments on three sequence labeling tasks, achieving the best reported results on two of them, and showing promising results on CoNLL03 NER.
Gregory Druck, Andrew McCallum
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Gregory Druck, Andrew McCallum
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