将法律规则纳入分类模型，提升判例推理的准确性与可解释性。
Rule-based Classifier Models
- 基于判例规则构建层级化分类框架，融合事实与法律规则。
- 通过规则约束实现新案件判决的自动推导，支持司法逻辑一致性。
- 适用于法律AI、判例分析系统，助力智能司法决策。
我们扩展了法律领域中分类模型的正式框架。现有分类框架仅依据案件事实进行表征，但法律推理本质上依赖于事实与规则（尤其是判决理由）。本文提出一种初步方法，将规则集融入分类模型。工作基于Canavotto等（2023）提出的层次化因素框架下的判例约束规则推理模型。我们展示了如何利用这一增强的规则驱动分类框架，对新案件做出判决推断，并举例说明时间要素与法院层级结构在新框架中的应用。
原文摘要 · Abstract (English)
We extend the formal framework of classifier models used in the legal domain. While the existing classifier framework characterises cases solely through the facts involved, legal reasoning fundamentally relies on both facts and rules, particularly the ratio decidendi. This paper presents an initial approach to incorporating sets of rules within a classifier. Our work is built on the work of Canavotto et al. (2023), which has developed the rule-based reason model of precedential constraint within a hierarchy of factors. We demonstrate how decisions for new cases can be inferred using this enriched rule-based classifier framework. Additionally, we provide an example of how the time element and the hierarchy of courts can be used in the new classifier framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。