Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

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 within …

We introduce a new pretraining approach geared for multi-document language modeling,
incorporating two key ideas into the masked language modeling self-supervised objective …

Coreference resolution has been mostly investigated within a single document scope,
showing impressive progress in recent years based on end-to-end models. However, the …

We study the problem of event coreference resolution (ECR) that seeks to group coreferent
event mentions into the same clusters. Deep learning methods have recently been applied …

Cross-document event coreference resolution (CD-ECR) is a task of clustering event
mentions across multiple documents that refer to the same real-world events. Previous …

Recent evaluation protocols for Cross-document (CD) coreference resolution have often
been inconsistent or lenient, leading to incomparable results across works and …

Event Coreference Resolution (ECR) is the task of linking mentions of the same event either
within or across documents. Most mention pairs are not coreferent, yet many that are …

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 …

Conventional event coreference systems commonly use a pipeline architecture and rely
heavily on handcrafted features, which often causes error propagation problems and leads …