The second is via supervised learning, where we explore different modeling techniques to capture the statistical regularities in creative compositional operations.
we will explore (1) compositional operations of vector space models, (2) kernels capturing the non-linear composition of different dimensions in the meaning space, (3) the use of neural networks as an alternative to incorporate nonlinearity in vector composition.