Fine-Grained Entity Typing for Domain Independent Entity Linking

Understand

Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in.

• For this problem, a domain is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset.
• We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution.
• Rather than predicting entities directly, our approach models fine-grained entity properties, which can help disambiguate between even closely related entities.