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Learning Flexible Features for Conditional Random Fields

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3 Author(s)
Liam Stewart ; Google, Mountain View, CA ; Xuming He ; Richard S. Zemel

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:30 ,  Issue: 8 )