Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4 in event detection and 6.1 in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9 improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities.

翻译：开发一个能够抽取海量事件类型的通用抽取系统是事件抽取领域长期追求的目标。实现这一目标面临两大挑战：1）缺乏高效且有效的标注方法；2）缺乏能够处理海量类型的强大抽取方法。针对第一个挑战，我们提出了一种基于大语言模型的协同标注方法。该方法通过多个大语言模型之间的协作，首先对远程监督得到的触发词标注进行精炼，随后进行论元标注。接着，通过投票阶段整合不同大语言模型的标注偏好。最终，我们构建了迄今为止规模最大的事件抽取数据集EEMT，该数据集包含超过20万个样本、3,465种事件类型以及6,297种角色类型。针对第二个挑战，我们提出了一种基于大语言模型的分区事件抽取方法，称为LLM-PEE。为了克服大语言模型有限的上下文长度，LLM-PEE首先召回候选事件类型，然后将其划分为多个分区供大语言模型进行事件抽取。在有监督设置下的实验结果表明，LLM-PEE在事件检测和论元抽取任务上分别比现有最优方法提升了5.4和6.1个点。在零样本设置下，LLM-PEE相较于主流大语言模型取得了最高12.9的性能提升，展现了其强大的泛化能力。