Grounded learning for compositional vector semantics
Martha Lewis
Categorical compositional distributional semantics is an approach to
modelling language that combines the success of vector-based models of meaning
with the compositional power of formal semantics. However, this approach was
developed without an eye to cognitive plausibility. Vector representations of
concepts and concept binding are also of interest in cognitive science, and
have been proposed as a way of representing concepts within a biologically
plausible spiking neural network. This work proposes a way for compositional
distributional semantics to be implemented within a spiking neural network
architecture, with the potential to address problems in concept binding, and
give a small implementation. We also describe a means of training word
representations using labelled images.