# EntEval: A Holistic Evaluation Benchmark for Entity Representations

arXiv: https://arxiv.org/abs/1909.00137 · HTML: https://mlsanity.com/paper/1909.00137/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections](https://mlsanity.com/paper/2102.13247/) (2021)
- [Biomedical Interpretable Entity Representations](https://mlsanity.com/paper/2106.09502/) (2021)
- [Connecting a French Dictionary from the Beginning of the 20th Century to Wikidata](https://mlsanity.com/paper/2206.11022/) (2022)
- [DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions](https://mlsanity.com/paper/2106.05365/) (2021)
- [Novel Aficionados and Doppelg"angers: a referential task for semantic representations of individual entities](https://mlsanity.com/paper/2104.10270/) (2021)

## Abstract (arXiv)

Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models and show that they improve strong baselines on multiple EntEval tasks.