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 this https URL .

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

LEEC: A Legal Element Extraction Dataset with an Extensive  Domain-Specific Label System

We present a Chinese judicial reading comprehension (CJRC) dataset which
contains approximately 10K documents and almost 50K questions with answers. The
documents come from judgment documents and the questions are annotated by law
experts. The CJRC dataset can help researchers extract elements by reading
comprehension technology. Element extraction is an important task in the legal
field. However, it is difficult to predefine the element types completely due
to the diversity of document types and causes of action. By contrast, machine
reading comprehension technology can quickly extract elements by answering
various questions from the long document. We build two strong baseline models
based on BERT and BiDAF. The experimental results show that there is enough
space for improvement compared to human annotators.

本研究介绍了一个含有近 10K 文档和近 50K 个问题及其答案的中国司法阅读理解 (CJRC) 数据集，基于 Bert 和 BiDAF 构建了两个强基准模型，实验结果显示与人工标注员的表现相比仍有进步的空间。该数据集可用于司法领域中元素提取的机器阅读理解技术研究。