  Vector space models have become popular in distributional semantics, despite
the challenges they face in capturing various semantic phenomena. We propose a
novel probabilistic framework which draws on both formal semantics and recent
advances in machine learning. In particular, we separate predicates from the
entities they refer to, allowing us to perform Bayesian inference based on
logical forms. We describe an implementation of this framework using a
combination of Restricted Boltzmann Machines and feedforward neural networks.
Finally, we demonstrate the feasibility of this approach by training it on a
parsed corpus and evaluating it on established similarity datasets.
