Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents.Annotating CDCR data is an arduous and expensive process, explaining why existing corpora are small and lack domain coverage.To overcome this bottleneck, we automatically extract event coreference data from hyperlinks in online news: When referring to a significant real-world event, writers often add a hyperlink to another article covering this event.We demonstrate that collecting hyperlinks which point to the same article(s) produces extensive and highquality CDCR data and create a corpus of 2M documents and 2.7M silver-standard event mentions called HyperCoref.We evaluate a state-of-the-art system on three CDCR corpora and find that models trained on small subsets of HyperCoref are highly competitive, with performance similar to models trained on goldstandard data.With our work, we free CDCR research from depending on costly humanannotated training data and open up possibilities for research beyond English CDCR, as our data extraction approach can be easily adapted to other languages.1

Cross-document event coreference resolution (CDCR) is an NLP task in which mentions of events need to be identified and clustered throughout a collection of documents. CDCR aims to benefit downstream multidocument applications, but despite recent progress on corpora and system development, downstream improvements from applying CDCR have not been shown yet. We make the observation that every CDCR system to date was developed, trained, and tested only on a single respective corpus. This raises strong concerns on their generalizability—a must-have for downstream applications where the magnitude of domains or event mentions is likely to exceed those found in a curated corpus. To investigate this assumption, we define a uniform evaluation setup involving three CDCR corpora: ECB+, the Gun Violence Corpus, and the Football Coreference Corpus (which we reannotate on token level to make our analysis possible). We compare a corpus-independent, feature-based system against a recent neural system developed for ECB+. Although being inferior in absolute numbers, the feature-based system shows more consistent performance across all corpora whereas the neural system is hit-or-miss. Via model introspection, we find that the importance of event actions, event time, and so forth, for resolving coreference in practice varies greatly between the corpora. Additional analysis shows that several systems overfit on the structure of the ECB+ corpus. We conclude with recommendations on how to achieve generally applicable CDCR systems in the future—the most important being that evaluation on multiple CDCR corpora is strongly necessary. To facilitate future research, we release our dataset, annotation guidelines, and system implementation to the public.1

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications.Despite the task's importance, research focus was given mostly to withindocument entity coreference, with rather little attention to the other variants.We propose a neural architecture for cross-document coreference resolution.Inspired by Lee et al. (2012), we jointly model entity and event coreference.We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures.Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus.Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.