# Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

## TL;DR

This paper introduces a novel method combining hyperbolic embeddings and Hearst patterns to infer accurate and consistent concept hierarchies from large text corpora, outperforming existing approaches.

## Contribution

It presents a new approach that leverages hyperbolic space for better hierarchy inference, including handling missing links and correcting errors, with improved efficiency and consistency.

## Key findings

- Achieves state-of-the-art performance on benchmark datasets.
- Effectively predicts missing is-a relationships.
- Enhances hierarchical consistency of inferred taxonomies.

## Abstract

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.