梳理法律领域数据集、评测基准与本体资源，助力智能法律系统研发。
Computational Law: Datasets, Benchmarks, and Ontologies
- 系统综述法律专用数据集与评测基准
- 整合法律本体资源以提升系统互操作性
- 为研究者提供最新可用资源参考
计算机科学与人工智能的进展正推动法律领域的数字化转型，相关论文与应用数量显著增长。机器学习与深度学习模型需大量领域特定数据以实现高性能，而本体等语义资源对构建大规模计算法律系统至关重要，可保障系统间互操作性。本文全面回顾了当前计算法律领域提出的数据集、评测基准与本体资源，旨在为研究人员与从业者开发和测试法律计算方法与系统提供有力支持。
原文摘要 · Abstract (English)
Recent developments in computer science and artificial intelligence have also contributed to the legal domain, as revealed by the number and range of related publications and applications. Machine and deep learning models require considerable amount of domain-specific data for training and comparison purposes, in order to attain high-performance in the legal domain. Additionally, semantic resources such as ontologies are valuable for building large-scale computational legal systems, in addition to ensuring interoperability of such systems. Considering these aspects, we present an up-to-date review of the literature on datasets, benchmarks, and ontologies proposed for computational law. We believe that this comprehensive and recent review will help researchers and practitioners when developing and testing approaches and systems for computational law.
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