# Contextualized End-to-End Neural Entity Linking

arXiv: https://arxiv.org/abs/1911.03834 · HTML: https://mlsanity.com/paper/1911.03834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

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

- [Unsupervised Matching of Data and Text](https://mlsanity.com/paper/2112.08776/) (2021)

## Abstract (arXiv)

We propose yet another entity linking model (YELM) which links words to entities instead of spans. This overcomes any difficulties associated with the selection of good candidate mention spans and makes the joint training of mention detection (MD) and entity disambiguation (ED) easily possible. Our model is based on BERT and produces contextualized word embeddings which are trained against a joint MD and ED objective. We achieve state-of-the-art results on several standard entity linking (EL) datasets.