# Data Augmentation for Hypernymy Detection

arXiv: https://arxiv.org/abs/2005.01854 · HTML: https://mlsanity.com/paper/2005.01854/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 7 of 7)

- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (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)
- [A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches](https://mlsanity.com/paper/2208.09012/) (2022)
- [Global Mixup: Eliminating Ambiguity with Clustering](https://mlsanity.com/paper/2206.02734/) (2022)
- [Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification](https://mlsanity.com/paper/2212.13939/) (2022)
- [I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection](https://mlsanity.com/paper/2308.04109/) (2023)
- [Inferring Adjective Hypernyms with Language Models to Increase the Connectivity of Open English Wordnet](https://mlsanity.com/paper/2506.10715/) (2025)

## Abstract (arXiv)

The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of state-of-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data. We have developed two novel data augmentation techniques which generate new training examples from existing ones. First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as "small dog - dog" or "small dog - animal", for which a hypernymy relationship can be assumed. Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed. We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as WordNet. Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.