Improving Entity Linking by Modeling Latent Entity Type Information

TL;DR

This paper enhances neural entity linking by incorporating latent entity type information using BERT, significantly improving accuracy on benchmarks by reducing type errors.

Contribution

It introduces a novel method to inject latent entity type info into embeddings and combines it with BERT-based similarity to improve linking accuracy.

Findings

Outperforms state-of-the-art on AIDA-CoNLL benchmark

Reduces type-related linking errors

Demonstrates effectiveness of latent type modeling

Abstract

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. Our model significantly outperforms the state-of-the-art entity linking models on standard benchmark (AIDA-CoNLL). Detailed experiment analysis demonstrates that our model corrects most of the type errors produced by the direct baseline.