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 …

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 …

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 …

The diverse relationships among real-world events, including coreference, temporal, causal,
and subevent relations, are fundamental to understanding natural languages. However, two …

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 aim to comprehensively identify all the event causal relations in a document, both within
a sentence and across sentences, which is important for reconstructing pivotal event …

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 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 …