Learning Flexible Features for Conditional Random Fields

Abstract-- Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features. These features can be functions of arbitrary combinations of observations, labels and auxiliary hidden variables. We also present a simple induction scheme to learn these features, which can automatically determine the complexity needed for a given data set. We apply the model to two real-world tasks, information extraction and image labeling, and compare our results to several other methods for discriminative labeling.

• Type: Journal
• Year: 2008
• Where: PAMI
• Authors: Liam Stewart, Xuming He, Richard S. Zemel
• Keywords: Auxiliary Hidden Variables, Discriminative Labeling, PAMI 2008, Undesirable Exponential Increase