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.

本文提出了使用 Poincaré 嵌入来改善领域特定分类法诱导的现有最先进方法，该方法可用作重新定位（预先诱导的）分类法中的错误下位词术语的信号，以及用于将断开的词术语附加到分类法中。本方法在 SemEval-2016 任务 13 的分类法提取上大大改进了以前的最先进结果，支持 Poincaré 嵌入优于分布语义表示的假设，因为它们可以更好地捕捉层次词汇语义关系。