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arxiv: 2603.05171 · v2 · pith:WRLGJNKVnew · submitted 2026-03-05 · 💻 cs.CL · cs.AI

Guidelines for the Annotation and Visualization of Legal Argumentation Structures in Chinese Judicial Decisions

classification 💻 cs.CL cs.AI
keywords legalguidelinereasoningstructuresannotationargumentationjudgmentjudicial
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Structural Dilemmas and Developmental Pathways of Legal Argument Mining in the Era of Artificial Intelligence

    cs.CL 2026-05 unverdicted novelty 3.0

    Legal argument mining advances slowly because no structured way exists to represent arguments that balances rich legal theory with what computers can process, creating dilemmas in data standardization, modeling, and d...