  Transformation-based learning has been successfully employed to solve many
natural language processing problems. It achieves state-of-the-art performance
on many natural language processing tasks and does not overtrain easily.
However, it does have a serious drawback: the training time is often
intorelably long, especially on the large corpora which are often used in NLP.
In this paper, we present a novel and realistic method for speeding up the
training time of a transformation-based learner without sacrificing
performance. The paper compares and contrasts the training time needed and
performance achieved by our modified learner with two other systems: a standard
transformation-based learner, and the ICA system \cite{hepple00:tbl}. The
results of these experiments show that our system is able to achieve a
significant improvement in training time while still achieving the same
performance as a standard transformation-based learner. This is a valuable
contribution to systems and algorithms which utilize transformation-based
learning at any part of the execution.
