Paper: Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction

ACL ID W13-3505
Title Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013
Authors

We present a flexible formulation of semi- supervised learning for structured mod- els, which seamlessly incorporates graph- based and more general supervision by ex- tending the posterior regularization (PR) framework. Our extension allows for any regularizer that is a convex, differentiable function of the appropriate marginals. We show that surprisingly, non-linearity of such regularization does not increase the complexity of learning, provided we use multiplicative updates of the structured ex- ponentiated gradient algorithm. We il- lustrate the extended framework by learn- ing conditional random fields (CRFs) with quadratic penalties arising from a graph Laplacian. On sequential prediction tasks of handwriting recognition and part-of- speech (POS) tagging, our method makes significant...