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Entity typing is the task of assigning semantic types to the entities thatare mentioned in a text. Since obtaining sufficient amounts of manualannotations is expensive, current state-of-the-art methods are typicallytrained on automatically labelled datasets, e.g. by exploiting links betweenWikipedia pages. In this paper, we propose to use coreference chains as anadditional supervision signal. Specifically, we pre-train an entity encoderusing a contrastive loss, such that entity embeddings of coreferring entitiesare more similar to each other than to the embeddings of other entities. Sincethis strategy is not tied to Wikipedia, we can pre-train our entity encoder onother genres than encyclopedic text and on larger amounts of data. Ourexperimental results show that the proposed pre-training strategy allows us toimprove the state-of-the-art in fine-grained entity typing, provided that onlyhigh-quality coreference links are exploited.

EnCore: Pre-Training Entity Encoders using Coreference Chains