In all cases, parameters are tuned from the training portion of the data.
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.