As a pivotal task in natural language processing, element extraction has gained significance in the legal domain. Extracting legal elements from judicial documents helps enhance interpretative and analytical capacities of legal cases, and thereby facilitating a wide array of downstream applications in various domains of law. Yet existing element extraction datasets are limited by their restricted access to legal knowledge and insufficient coverage of labels. To address this shortfall, we introduce a more comprehensive, large-scale criminal element extraction dataset, comprising 15,831 judicial documents and 159 labels. This dataset was constructed through two main steps: First, designing the label system by our team of legal experts based on prior legal research which identified critical factors driving and processes generating sentencing outcomes in criminal cases; Second, employing the legal knowledge to annotate judicial documents according to the label system and annotation guideline. The Legal Element ExtraCtion dataset (LEEC) represents the most extensive and domain-specific legal element extraction dataset for the Chinese legal system. Leveraging the annotated data, we employed various SOTA models that validates the applicability of LEEC for Document Event Extraction (DEE) task. The LEEC dataset is available on https://github.com/THUlawtech/LEEC .

在法律领域中，元素提取作为自然语言处理的重要任务之一，从司法文件中提取法律要素有助于增强对法律案例的解释和分析能力，进而促进法律领域各个方面的下游应用。通过引入一个更全面、大规模的刑事法律要素提取数据集，本研究解决了现有数据集的限制问题，该数据集由15831个司法文件和159个标签组成，并通过专家设计的标签系统和注释准则进行构建。LEEC数据集是目前为中国法律系统最广泛且专业的法律要素提取数据集，利用标注的数据，我们运用各种最先进的模型验证了LEEC在文档事件提取任务中的适用性。

LEEC：一份带有广泛领域特定标签系统的法律元素提取数据集