What is the Dimensionality of Human Semantic Space?

McDonald and Lowe [15] showed that cosines in a semantic space of several hundred dimensions reflect human priming results for a wide range of semantic and associatively related words [16, Exp.2]. Previously, Lowe [II, 10] argued that the intrinsic dimensionality of semantic space is much lower, and that high-dimensional structure can be effectively captured in just two dimensions as the surface of a neural map. This paper provides a replication of McDonald and Lowe’s results in two dimensions using the Generative Topographic Mapping [2], a statistically motivated neural network architecture for topographic maps.

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