Paper: Coaxing Confidences From An Old Freind: Probabilistic Classifications From Transformation Rule Lists

ACL ID W00-1304
Title Coaxing Confidences From An Old Freind: Probabilistic Classifications From Transformation Rule Lists
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 2000
Authors

Transformation-based learning has been success- fully employed to solve many natural language processing problems. It has many positive fea- tures, 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 ex- periments are presented which measure the model- ing 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 1, the estimates produced by this technique are more informative than those g...