  We introduce a significant improvement for a relatively new machine learning
method called Transformation-Based Learning. By applying a Monte Carlo strategy
to randomly sample from the space of rules, rather than exhaustively analyzing
all possible rules, we drastically reduce the memory and time costs of the
algorithm, without compromising accuracy on unseen data. This enables
Transformation- Based Learning to apply to a wider range of domains, as it can
effectively consider a larger number of different features and feature
interactions in the data. In addition, the Monte Carlo improvement decreases
the labor demands on the human developer, who no longer needs to develop a
minimal set of rule templates to maintain tractability.
