Event Detection (ED) aims to recognize mentions of events (ie, event triggers) and their
types in text. Recently, several ED datasets in various domains have been proposed …

Event trigger detection, entity mention recognition, event argument extraction, and relation
extraction are the four important tasks in information extraction that have been performed …

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs)
by fine-tuning. However, existing pre-training methods have not involved modeling event …

Event extraction is typically modeled as a multi-class classification problem where event
types and argument roles are treated as atomic symbols. These approaches are usually …

Existing works on information extraction (IE) have mainly solved the four main tasks
separately (entity mention recognition, relation extraction, event trigger detection, and …

We consider the problem of collectively detecting multiple events, particularly in cross-
sentence settings. The key to dealing with the problem is to encode semantic information …

Event extraction has gained considerable interest due to its wide-ranging applications.
However, recent studies draw attention to evaluation issues, suggesting that reported scores …

The EAE task extracts a structured event record from an event text. Most existing approaches
train the EAE model on each dataset independently and ignore the overlap knowledge …

Event extraction is an important task in natural language processing that focuses on mining
event-related information from unstructured text. Despite considerable advancements, it is …

Event detection (ED) is a critical subtask of event extraction that seeks to identify event
triggers of certain types in texts. Despite significant advances in ED, existing methods …