Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs

Abstract

We introduce a new perception-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses pointwise label prediction, large features, including arbitrary number of hidden variables, can be incorporated with polynomial time complexity. This is in contrast to existing labelers that can handle only features of a small number of hidden variables, such as Maximum Entropy Markov Models and Conditional Random Fields. We also introduce several kernel functions for labeling sequences, trees, and graphs and efficient algorithms for them.