| y (2007) | |||||||||||||||
Abstract | |||||||||||||||
| 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 classier. Three experiments are presented which measure the modeling accuracy and cross-entropy of the probabilistic classier on unseen data and the degree to which the output probabilities from the classier can be used to estimate condences in its classication 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 generated by a state-of-the-art decision tree. 1 | |||||||||||||||
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