I'll discuss some results on the question of how semantic relationships familiar to humans as geometric relationships between their activations. The essential idea is that the exponentiated dot product form used by softmax distributions combined with the requirement that models can compose `causally seperable' concepts imposes non-trivial geometric structure. In particular, we will see that information geometry---particularly, the geometry of exponential family distributions---plays an important role.