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.
In SenseClusters native mode, a word co-occurrence matrix is compiled, and that is used to provide word vectors that replace each of the words in a headless context. These word vectors are averaged together to create a representation of that context. The premise is that contexts that are made up of words that occur with some of the same other words will be similar to each other.