# Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment

arXiv: https://arxiv.org/abs/1605.05433 · HTML: https://mlsanity.com/paper/1605.05433/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2016.

## Built on by (most cited first, 4 of 4)

- [Distilling Relation Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2110.15705/) (2021)
- [ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicality](https://mlsanity.com/paper/2110.06376/) (2021)
- [Language Models for Lexical Inference in Context](https://mlsanity.com/paper/2102.05331/) (2021)
- [Cross-Modal Taxonomic Generalization in (Vision-) Language Models](https://mlsanity.com/paper/2603.07474/) (2026)

## Abstract (arXiv)

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.