  Datasets and methods for cross-document coreference resolution (CDCR) focus
on events or entities with strict coreference relations. They lack, however,
annotating and resolving coreference mentions with more abstract or loose
relations that may occur when news articles report about controversial and
polarized events. Bridging and loose coreference relations trigger associations
that may lead to exposing news readers to bias by word choice and labeling. For
example, coreferential mentions of "direct talks between U.S. President Donald
Trump and Kim" such as "an extraordinary meeting following months of heated
rhetoric" or "great chance to solve a world problem" form a more positive
perception of this event. A step towards bringing awareness of bias by word
choice and labeling is the reliable resolution of coreferences with high
lexical diversity. We propose an unsupervised method named XCoref, which is a
CDCR method that capably resolves not only previously prevalent entities, such
as persons, e.g., "Donald Trump," but also abstractly defined concepts, such as
groups of persons, "caravan of immigrants," events and actions, e.g., "marching
to the U.S. border." In an extensive evaluation, we compare the proposed XCoref
to a state-of-the-art CDCR method and a previous method TCA that resolves such
complex coreference relations and find that XCoref outperforms these methods.
Outperforming an established CDCR model shows that the new CDCR models need to
be evaluated on semantically complex mentions with more loose coreference
relations to indicate their applicability of models to resolve mentions in the
"wild" of political news articles.
