  Cross-document event coreference resolution is a foundational task for NLP
applications involving multi-text processing. However, existing corpora for
this task are scarce and relatively small, while annotating only modest-size
clusters of documents belonging to the same topic. To complement these
resources and enhance future research, we present Wikipedia Event Coreference
(WEC), an efficient methodology for gathering a large-scale dataset for
cross-document event coreference from Wikipedia, where coreference links are
not restricted within predefined topics. We apply this methodology to the
English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our
dataset creation method is generic and can be applied with relatively little
effort to other Wikipedia languages. To set baseline results, we develop an
algorithm that adapts components of state-of-the-art models for within-document
coreference resolution to the cross-document setting. Our model is suitably
efficient and outperforms previously published state-of-the-art results for the
task.
