# Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings

arXiv: https://arxiv.org/abs/1906.02002 · HTML: https://mlsanity.com/paper/1906.02002/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 23 of 23)

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- [Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion](https://mlsanity.com/paper/2507.23508/) (2025)
- [Taxonomy Enrichment with Text and Graph Vector Representations](https://mlsanity.com/paper/2201.08598/) (2022)
- [DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space](https://mlsanity.com/paper/2302.11165/) (2023)

## Abstract (arXiv)

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.