Headless clustering takes as input contexts that do not contain a target
or head word. The entire context must be considered during clustering, as
there is no target word around which to adjust the test or training
scope, for example. (These are options in discriminate.pl)
Typical examples of headless contexts include email or other short messages or documents, where the goal is to cluster them based on topic. Note that in addition to test_scope and target_scope, target co-occurrence (tco) features are not supported since there are no target words in the contexts.
When using LSA to carry out headless context clustering, each feature will be represented as a vector that shows the contexts in which it occurs. Each context will be represented as the average of the vectors associated with the features that occur within that context. The premise is that contexts that are made up of features that occur in some of the same contexts should be similar to each other.