中国司法判决中法律论证结构的标注与可视化指南
Guidelines for the Annotation and Visualization of Legal Argumentation Structures in Chinese Judicial Decisions
AI总结：
提出一个系统化、可操作的标注框架，用于表示司法判决中的法律论证结构，支持大规模司法推理分析和法律论证挖掘。
摘要：
本指南提出了一个系统化且可操作的标注框架，用于表示司法判决中的法律论证结构。该框架基于法律推理和论证理论，旨在揭示司法推理的逻辑组织，并为计算分析提供可靠基础。在元素层面，本指南区分了非命题层和命题层。非命题层由两个元素组成：议题和非论证性成分。在命题层面，本指南定义了四种命题类型：一般规范性判断、特殊规范性判断、一般事实判断和特殊事实判断。在关系层面，定义了五种关系类型来表示论证结构：支持、攻击、联合、匹配和同一性。这些关系捕捉了正面和负面的论证连接、合取推理结构、法律规范与案件事实之间的对应关系，以及命题之间的同一性或语义等价性。本指南进一步规定了基本结构和嵌套结构的形式化表示规则和可视化约定，使得复杂论证模式的可视化保持一致。此外，它建立了标准化的标注工作流程和一致性控制机制，以确保标注数据的可重复性和可靠性。通过提供清晰的概念模型、形式化表示规则和实用的标注程序，本指南支持大规模司法推理分析以及未来在法律论证挖掘、法律推理计算建模和人工智能辅助法律分析方面的研究。
英文摘要：
This Guideline presents a systematic and operationalizable annotation framework for representing legal argumentation structures in judicial decisions. Grounded in theories of legal reasoning and argumentation, the framework aims to reveal the logical organization of judicial reasoning and provide a reliable foundation for computational analysis. At the element level, the Guideline distinguishes between the non-propositional layer and the propositional layer. The non-propositional layer consists of two elements: Issue and Non-argumentative Component. At the propositional level, the Guideline defines four proposition types: General Normative Judgment, Particular Normative Judgment, General Factual Judgment, and Particular Factual Judgment. At the relational level, five relation types are defined to represent argumentative structures: Support, Attack, Joint, Match, and Identity. These relations capture positive and negative argumentative connections, conjunctive reasoning structures, correspondences between legal norms and case facts, and identity or semantic equivalence between propositions. The Guideline further specifies formal representation rules and visualization conventions for both basic and nested structures, enabling consistent visualization of complex argumentation patterns. In addition, it establishes a standardized annotation workflow and consistency control mechanisms to ensure the reproducibility and reliability of annotated data. By providing a clear conceptual model, formal representation rules, and practical annotation procedures, this Guideline supports large-scale analysis of judicial reasoning and future research in legal argument mining, computational modeling of legal reasoning, and AI-assisted legal analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
