We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks.

本文提出了一种新方法， 结合了双曲嵌入与 Hearst 模式，用于从大型文本语料库中推断概念层次结构和 is-a 关系。实验表明，该方法在多个基准测试中取得了最先进的性能。

通过双曲嵌入从文本语料库中推导概念层次结构

Inferring Concept Hierarchies from Text Corpora via Hyperbolic  Embeddings

We consider the task of predicting lexical entailment using distributional
vectors. We perform a novel qualitative analysis of one existing model which
was previously shown to only measure the prototypicality of word pairs. We find
that the model strongly learns to identify hypernyms using Hearst patterns,
which are well known to be predictive of lexical relations. We present a novel
model which exploits this behavior as a method of feature extraction in an
iterative procedure similar to Principal Component Analysis. Our model combines
the extracted features with the strengths of other proposed models in the
literature, and matches or outperforms prior work on multiple data sets.

本文研究使用分布式向量预测词汇蕴涵的任务，提出基于 Hearst patterns 的特征提取方法，并结合文献中已有的优秀模型，成功预测多个数据集的结果。