  Several studies on sentence processing suggest that the mental lexicon keeps
track of the mutual expectations between words. Current DSMs, however,
represent context words as separate features, thereby loosing important
information for word expectations, such as word interrelations. In this paper,
we present a DSM that addresses this issue by defining verb contexts as joint
syntactic dependencies. We test our representation in a verb similarity task on
two datasets, showing that joint contexts achieve performances comparable to
single dependencies or even better. Moreover, they are able to overcome the
data sparsity problem of joint feature spaces, in spite of the limited size of
our training corpus.
