In Defense of Spatial Models of Lexical Semantics


Semantic space models of lexical semantics learn vector representations for words by observing statistical redundancies in a text corpus. A word’s meaning is represented as a point in a high-dimensional semantic space. However, these spatial models have difficulty simulating human free association data due to the constraints placed upon them by metric axioms which appear to be violated in association norms. Here, we build on work by Griffiths, Steyvers, and Tenenbaum (2007) and test the ability of spatial semantic models to simulate association data when they are fused with a Luce choice rule to simulate the process of selecting a response in free association. The results provide an existence proof that spatial models can produce the patterns of data in free association previously thought to be problematic.

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