  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.
We derive a large inventory of types (tens of thousands) from Wikipedia
categories, and use hyperlinked mentions in Wikipedia to distantly label data
and train an entity typing model. At test time, we classify a mention with this
typing model and use soft type predictions to link the mention to the most
similar candidate entity. We evaluate our entity linking system on the
CoNLL-YAGO dataset (Hoffart et al., 2011) and show that our approach
outperforms prior domain-independent entity linking systems. We also test our
approach in a harder setting derived from the WikilinksNED dataset (Eshel et
al., 2017) where all the mention-entity pairs are unseen during test time.
Results indicate that our approach generalizes better than a state-of-the-art
neural model on the dataset.
