Similarity modeling - Similarity modeling uses the same parameters as distributional analysis, but it assumes nice clusters are not possible. Instead it makes ad-hoc analogies by averaging sets of properties, as required:
E.g. Dagan, Marcus, Markovitch '95: (p.g. 32, long version) "It has been
traditionally assumed that ... information about words should be
generalized using word classes ... However, it was never clearly shown
that unrestricted language is indeed structured in accordance with
(and previously on p.g. 4) "... our method assumes that
generalizations should be minimized. Information is thus kept at a
maximal level of detail, and missing information is deduced by the
most specific analogies, which are carried out whenever needed."
Dagan, Ido, Shaul Marcus and Shaul Markovitch. Contextual word similarity and estimation from sparse data, Computer, Speech and Language, 1995, Vol. 9, pp. 123-152.