Abstraction to a single prototypical representation is a core
principle of Distributional Semantic Models (DSMs) that
learn semantic representations for words by applying
dimension reduction to statistical redundancies in language.
While the learning mechanisms for semantic abstraction vary
widely across the many DSMs in the literature, they are
essentially all prototype models in that they create a single
abstract representation for a word’s meaning. The prototype
method stands in stark contrast to work in the field of
categorization that has converged on the importance of
instance models. In comparison to the prototype method,
instance-based models assume only an episodic store and,
rather than applying abstraction mechanisms at learning,
argue that meaning emerges in the act of retrieval. We cash
this idea out by presenting and evaluating an instance theory
of distributional semantics, and by demonstrating that it can
explain diverging patterns of homonymous words that classic
“abstraction-at-learning” models simply cannot as a
consequence of their architectural assumptions.