Event extraction is challenging due to the complex structure of event records and the
semantic gap between text and event. Traditional methods usually extract event records by …

Event extraction has long been a challenging task, addressed mostly with supervised
methods that require expensive annotation and are not extensible to new event ontologies …

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 …

Large language models have recently risen in popularity due to their ability to perform many
natural language tasks without requiring any fine-tuning. In this work, we focus on two novel …

Abstract Information extraction tasks such as event extraction require an in-depth
understanding of the output structure and sub-task dependencies. They heavily rely on task …

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that
can be applied to a large variety of newsworthy scenarios. The framework consists of two …

Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes
two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we …

Event detection is a classic natural language processing task. However, the constantly
emerging new events make supervised methods not applicable to unseen types. Previous …

Recent works in Event Argument Extraction (EAE) have focused on improving model
generalizability to cater to new events and domains. However, standard benchmarking …

Determining the role of event arguments is a crucial subtask of event extraction. Most
previous supervised models leverage costly annotations, which is not practical for open …