  Detecting hypernymy relations is a key task in NLP, which is addressed in the
literature using two complementary approaches. Distributional methods, whose
supervised variants are the current best performers, and path-based methods,
which received less research attention. We suggest an improved path-based
algorithm, in which the dependency paths are encoded using a recurrent neural
network, that achieves results comparable to distributional methods. We then
extend the approach to integrate both path-based and distributional signals,
significantly improving upon the state-of-the-art on this task.
