Measuring the similarity of short written contexts is a fundamental problem
in Natural Language Processing. This article provides a unifying framework by
which short context problems can be categorized both by their intended
application and proposed solution. The goal is to show that various problems
and methodologies that appear quite different on the surface are in fact very
closely related. The axes by which these categorizations are made include the
format of the contexts (headed versus headless), the way in which the contexts
are to be measured (first-order versus second-order similarity), and the
information used to represent the features in the contexts (micro versus macro
views). The unifying thread that binds together many short context applications
and methods is the fact that similarity decisions must be made between contexts
that share few (if any) words in common.Comment: 23 page
