  We consider the task of document-level entity linking (EL), where it is
important to make consistent decisions for entity mentions over the full
document jointly. We aim to leverage explicit "connections" among mentions
within the document itself: we propose to join the EL task with that of
coreference resolution (coref). This is complementary to related works that
exploit either (i) implicit document information (e.g., latent relations among
entity mentions, or general language models) or (ii) connections between the
candidate links (e.g, as inferred from the external knowledge base).
Specifically, we cluster mentions that are linked via coreference, and enforce
a single EL for all of the clustered mentions together. The latter constraint
has the added benefit of increased coverage by joining EL candidate lists for
the thus clustered mentions. We formulate the coref+EL problem as a structured
prediction task over directed trees and use a globally normalized model to
solve 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 in
their candidate entity list, we obtain a +50% increase in accuracy.
