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 …

Event is a common but non-negligible knowledge type. How to identify events from texts,
extract their arguments, even analyze the relations between different events are important …

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

Understanding discourse structures of news articles is vital to effectively contextualize the
occurrence of a news event. To enable computational modeling of news structures, we apply …

An important problem of Information Extraction involves Event Causality Identification (ECI)
that seeks to identify causal relation between pairs of event mentions. Prior models for ECI …

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 …

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

Recognizing the internal structure of events is a challenging language processing task of
great importance for text understanding. We present a supervised model for automatically …