  We present a neural framework for learning associations between interrelated
groups of words such as the ones found in Subject-Verb-Object (SVO) structures.
Our model induces a joint function-specific word vector space, where vectors of
e.g. plausible SVO compositions lie close together. The model retains
information about word group membership even in the joint space, and can
thereby effectively be applied to a number of tasks reasoning over the SVO
structure. We show the robustness and versatility of the proposed framework by
reporting state-of-the-art results on the tasks of estimating selectional
preference and event similarity. The results indicate that the combinations of
representations learned with our task-independent model outperform
task-specific architectures from prior work, while reducing the number of
parameters by up to 95%.
