A Three-Stage Framework for Event-Event Relation Extraction with Large Language Model

Feng Huang, Qiang Huang, Yuetong Zhao, Zhixiao Qi, Bingkun Wang, Yongfeng Huang, Songbin Li

Abstract: Expanding the parameter count of a large language model (LLM) alone is insufficient to achieve satisfactory outcomes in natural language processing tasks, specifically event extraction (EE), event temporal relation extraction (ETRE), and event causal relation extraction (ECRE). To tackle these challenges, we propose a novel three-stage extraction framework (ThreeEERE) that integrates an improved automatic chain of thought prompting (Auto-CoT) with LLM and is tailored based on a golden rule to maximize event and relation extraction precision. The three stages include constructing examples in each category, federating local knowledge to extract relationships between events, and selecting the best answer. By following these stages, we can achieve our objective. Although supervised models dominate for these tasks, our experiments on three types of extraction tasks demonstrate that utilizing these three stages approach yields significant results in event extraction and event relation extraction, even surpassing some supervised model methods in the extraction task.