# Negative Sampling Improves Hypernymy Extraction Based on Projection Learning

arXiv: https://arxiv.org/abs/1707.03903 · HTML: https://mlsanity.com/paper/1707.03903/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Technological taxonomies for hypernym and hyponym retrieval in patent texts](https://mlsanity.com/paper/2212.06039/) (2022)

## Abstract (arXiv)

We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.