Improving Entity Linking by Modeling Latent Entity Type Information

Understand

Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.

• However, the latent entity type information in the immediate context of the mention is neglected, which causes the models often link mentions to incorrect entities with incorrect type.
• To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT.
• In addition, we integrate a BERT-based entity similarity score into the local context model of a state-of-the-art model to better capture latent entity type information.