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 1, the estimates produced by this technique are more informative than those generated by a state-of-the-art decision tree.

In this paper we describe a technique for modeling and implementing process knowledge within an organization. We begin by presenting a framework (Knowledge Mill) for describing the knowledge management process. Later, we elaborate on one aspect of the process — classification. In particular, we describe a goal-oriented modeling schema for capturing and organizing knowledge during the decision-making process. A patented tool (ThoughtFlow™) that supports the application of the goal-oriented schema is also described. In addition, a case study of using the framework and tool in a strategy deployment process, within the IT organization of a large company, is presented.

String transformation systems have been introduced in (Brill, 1995) and have several applications in natural language processing. In this work we consider the computational problem of automatically learning from a given corpus the set of transformations presenting the best evidence. We introduce an original data structure and efficient algorithms that learn some families of transformations that are relevant for part-of-speech tagging and phonological rule systems. We also show that the same learning problem becomes NP-hard in cases of an unbounded use of don't care symbols in a transformation.