  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 .

Computation and Language;Information Retrieval