Contextualized End-to-End Neural Entity Linking.

We propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We achieve stateof-the-art results across a standard EL dataset using our model; we also study our model's performance under the setting when hand-crafted entity candidate sets are not available and find that the model performs well under such a setting also.