  We introduce the use of Poincar\'e embeddings to improve existing
state-of-the-art approaches to domain-specific taxonomy induction from text as
a signal for both relocating wrong hyponym terms within a (pre-induced)
taxonomy as well as for attaching disconnected terms in a taxonomy. This method
substantially improves previous state-of-the-art results on the SemEval-2016
Task 13 on taxonomy extraction. We demonstrate the superiority of Poincar\'e
embeddings over distributional semantic representations, supporting the
hypothesis that they can better capture hierarchical lexical-semantic
relationships than embeddings in the Euclidean space.
