  Common-sense reasoning is becoming increasingly important for the advancement
of Natural Language Processing. While word embeddings have been very
successful, they cannot explain which aspects of 'coffee' and 'tea' make them
similar, or how they could be related to 'shop'. In this paper, we propose an
explicit word representation that builds upon the Distributional Hypothesis to
represent meaning from semantic roles, and allow inference of relations from
their meshing, as supported by the affordance-based Indexical Hypothesis. We
find that our model improves the state-of-the-art on unsupervised word
similarity tasks while allowing for direct inference of new relations from the
same vector space.
