# Exploring Neural Entity Representations for Semantic Information

arXiv: https://arxiv.org/abs/2011.08951 · HTML: https://mlsanity.com/paper/2011.08951/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora](https://mlsanity.com/paper/2103.11029/) (2021)

## Abstract (arXiv)

Neural methods for embedding entities are typically extrinsically evaluated on downstream tasks and, more recently, intrinsically using probing tasks. Downstream task-based comparisons are often difficult to interpret due to differences in task structure, while probing task evaluations often look at only a few attributes and models. We address both of these issues by evaluating a diverse set of eight neural entity embedding methods on a set of simple probing tasks, demonstrating which methods are able to remember words used to describe entities, learn type, relationship and factual information, and identify how frequently an entity is mentioned. We also compare these methods in a unified framework on two entity linking tasks and discuss how they generalize to different model architectures and datasets.