  The compositionality degree of multiword expressions indicates to what extent
the meaning of a phrase can be derived from the meaning of its constituents and
their grammatical relations. Prediction of (non)-compositionality is a task
that has been frequently addressed with distributional semantic models. We
introduce a novel technique to blend hierarchical information with
distributional information for predicting compositionality. In particular, we
use hypernymy information of the multiword and its constituents encoded in the
form of the recently introduced Poincar\'e embeddings in addition to the
distributional information to detect compositionality for noun phrases. Using a
weighted average of the distributional similarity and a Poincar\'e similarity
function, we obtain consistent and substantial, statistically significant
improvement across three gold standard datasets over state-of-the-art models
based on distributional information only. Unlike traditional approaches that
solely use an unsupervised setting, we have also framed the problem as a
supervised task, obtaining comparable improvements. Further, we publicly
release our Poincar\'e embeddings, which are trained on the output of
handcrafted lexical-syntactic patterns on a large corpus.
