  Transformation-based learning has been successfully employed to solve many
natural language processing problems. It has many positive features, but one
drawback is that it does not provide estimates of class membership
probabilities.
  In this paper, we present a novel method for obtaining class membership
probabilities from a transformation-based rule list classifier. Three
experiments are presented which measure the modeling accuracy and cross-entropy
of the probabilistic classifier on unseen data and the degree to which the
output probabilities from the classifier can be used to estimate confidences in
its classification decisions.
  The results of these experiments show that, for the task of text chunking,
the estimates produced by this technique are more informative than those
generated by a state-of-the-art decision tree.
