Abstract
Cognitive scientists have long used distributional semantic
representations of categories. The predominant approach uses
distributional representations of category-denoting nouns, like
"city" for the category city. We propose a novel scheme that
represents categories as prototypes over representations of
names of its members, such as "Barcelona", "Mumbai",
and "Wuhan" for the category city. This name-based representation
empirically outperforms the noun-based representation on two
experiments (modelling human judgments of category relatedness and
predicting category membership) with particular improvements for
ambiguous nouns. We discuss the model complexity of both classes of
models and argue that the name-based model has superior explanatory
potential with regard to concept acquisition.
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