Recent years have seen a gradual shift of focus from entity-based tasks to event-based tasks
in information extraction research. Being a core event-based task, event coreference …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Despite recent promising results on the application of span-based models for event
reference interpretation, there is a lack of understanding of what has been improved. We …

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 …

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 present an approach to event coreference resolution by developing a general framework
for clustering that uses supervised representation learning. We propose a neural network …

We introduce a novel iterative approach for event coreference resolution that gradually
builds event clusters by exploiting inter-dependencies among event mentions within the …

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 …