  In this paper, we review five heuristic strategies for handling
context-sensitive features in supervised machine learning from examples. We
discuss two methods for recovering lost (implicit) contextual information. We
mention some evidence that hybrid strategies can have a synergetic effect. We
then show how the work of several machine learning researchers fits into this
framework. While we do not claim that these strategies exhaust the
possibilities, it appears that the framework includes all of the techniques
that can be found in the published literature on contextsensitive learning.
