  We present a model for compositional distributional semantics related to the
framework of Coecke et al. (2010), and emulating formal semantics by
representing functions as tensors and arguments as vectors. We introduce a new
learning method for tensors, generalising the approach of Baroni and Zamparelli
(2010). We evaluate it on two benchmark data sets, and find it to outperform
existing leading methods. We argue in our analysis that the nature of this
learning method also renders it suitable for solving more subtle problems
compositional distributional models might face.
