Document-Level Event Extraction with Definition-Driven ICL

The field of Natural Language Processing (NLP) has made significant strides in parsing and understanding human language, with event extraction technology playing a crucial role. Event extraction identifies events and their core elements from unstructured text data, detailing participants ("who"), time ("when"), location ("where"), event description ("what"), cause ("why"), and manner ("how"). Accurately extracting this information is essential for tasks such as text summarization, knowledge graph construction, intelligent question answering systems, and recommendation systems. Event extraction consists primarily of two subtasks: Event Detection (ED) and Event Argument Role Extraction (EAE). Event Detection aims to identify mentioned events in text, while Event Argument Role Extraction further identifies entities involved in the events and their respective roles. Currently, using large-scale pre-trained language models (LLMs) for closed-domain document-level event extraction has become mainstream in the field. In the field of event extraction in Natural Language Processing (NLP), despite significant advancements, several critical issues remain to be addressed.