# Hierarchical Embeddings for Hypernymy Detection and Directionality

arXiv: https://arxiv.org/abs/1707.07273 · HTML: https://mlsanity.com/paper/1707.07273/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2017.

## Built on by (most cited first, 19 of 19)

- [Hyperbolic Image-Text Representations](https://mlsanity.com/paper/2304.09172/) (2023)
- [Compositional Entailment Learning for Hyperbolic Vision-Language Models](https://mlsanity.com/paper/2410.06912/) (2024)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [Emergent Visual-Semantic Hierarchies in Image-Text Representations](https://mlsanity.com/paper/2407.08521/) (2024)
- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning](https://mlsanity.com/paper/2205.13371/) (2022)
- [PHyCLIP: ℓ1-Product of Hyperbolic Factors Unifies Hierarchy and Compositionality in Vision-Language Representation Learning](https://mlsanity.com/paper/2510.08919/) (2025)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [Yseop at FinSim-3 Shared Task 2021: Specializing Financial Domain Learning with Phrase Representations](https://mlsanity.com/paper/2108.09485/) (2021)
- [A Survey On Neural Word Embeddings](https://mlsanity.com/paper/2110.01804/) (2021)
- [HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings](https://mlsanity.com/paper/2204.02058/) (2022)
- [HCCM: Hierarchical Cross-Granularity Contrastive and Matching Learning for Natural Language-Guided Drones](https://mlsanity.com/paper/2508.21539/) (2025)
- [ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context](https://mlsanity.com/paper/2104.01523/) (2021)
- [Lexical semantics enhanced neural word embeddings](https://mlsanity.com/paper/2210.00754/) (2022)
- [Probing Taxonomic and Thematic Embeddings for Taxonomic Information](https://mlsanity.com/paper/2301.10656/) (2023)
- [Order Embeddings from Merged Ontologies using Sketching](https://mlsanity.com/paper/2101.02158/) (2021)
- [Text Data Integration](https://mlsanity.com/paper/2603.27055/) (2026)
- [HSG: Hyperbolic Scene Graph](https://mlsanity.com/paper/2604.17454/) (2026)
- [GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views](https://mlsanity.com/paper/2608.09270/) (2026)

## Abstract (arXiv)

We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym-hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state-of-the-art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.