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We consider the task of document-level entity linking (EL), where it isimportant to make consistent decisions for entity mentions over the fulldocument jointly. We aim to leverage explicit "connections" among mentionswithin the document itself: we propose to join the EL task with that ofcoreference resolution (coref). This is complementary to related works thatexploit either (i) implicit document information (e.g., latent relations amongentity mentions, or general language models) or (ii) connections between thecandidate links (e.g, as inferred from the external knowledge base).Specifically, we cluster mentions that are linked via coreference, and enforcea single EL for all of the clustered mentions together. The latter constrainthas the added benefit of increased coverage by joining EL candidate lists forthe thus clustered mentions. We formulate the coref+EL problem as a structuredprediction task over directed trees and use a globally normalized model tosolve it. Experimental results on two datasets show a boost of up to +5%F1-score on both coref and EL tasks, compared to their standalone counterparts.For a subset of hard cases, with individual mentions lacking the correct EL intheir candidate entity list, we obtain a +50% increase in accuracy.

Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution