  Previous research has demonstrated that Distributional Semantic Models (DSMs)
are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009)
and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is
surprising since DSMs notoriously lack perceptual grounding (De Vega et al.,
2012). In this paper we investigate the statistical sources required in
language to infer maps, and resulting constraints placed on mechanisms of
semantic representation. Study 1 brings word co-occurrence under experimental
control to demonstrate that direct co-occurrence in language is necessary for
traditional DSMs to successfully reproduce maps. Study 2 presents an
instance-based DSM that is capable of reconstructing maps independent of the
frequency of co-occurrence of city names.
