# Inferring Adjective Hypernyms with Language Models to Increase the Connectivity of Open English Wordnet

arXiv: https://arxiv.org/abs/2506.10715 · HTML: https://mlsanity.com/paper/2506.10715/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment](https://mlsanity.com/paper/1608.02117/) (2016)
- [Distilling Relation Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2110.15705/) (2021)
- [Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models](https://mlsanity.com/paper/2411.14103/) (2024)
- [Data Augmentation for Hypernymy Detection](https://mlsanity.com/paper/2005.01854/) (2020)
- [KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification](https://mlsanity.com/paper/2002.10903/) (2020)

## Abstract (arXiv)

Open English Wordnet is a key resource published in OntoLex-lemon as part of the linguistic linked open data cloud. There are, however, many links missing in the resource, and in this paper, we look at how we can establish hypernymy between adjectives. We present a theoretical discussion of the hypernymy relation and how it differs for adjectives in contrast to nouns and verbs. We develop a new resource for adjective hypernymy and fine-tune large language models to predict adjective hypernymy, showing that the methodology of TaxoLLaMa can be adapted to this task.