  Modelling compositionality has been a longstanding area of research in the
field of vector space semantics. The categorical approach to compositionality
maps grammar onto vector spaces in a principled way, but comes under fire for
requiring the formation of very high-dimensional matrices and tensors, and
therefore being computationally infeasible. In this paper I show how a linear
simplification of recursive neural tensor network models can be mapped directly
onto the categorical approach, giving a way of computing the required matrices
and tensors. This mapping suggests a number of lines of research for both
categorical compositional vector space models of meaning and for recursive
neural network models of compositionality.
