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

2026-03-05

AI Summary

This study addresses the lack of systematic annotation and visualization methods for legal argumentation structures in Chinese judicial judgments, which hinders computational analysis of legal reasoning. Drawing on legal reasoning theory, the work proposes the first fine-grained and operational annotation framework tailored to Chinese judgments. It formally defines four types of propositions, five categories of argumentative relations, formal representation rules for nested structures, and corresponding visualization conventions. A standardized annotation protocol and inter-annotator consistency control mechanism are also developed. The resulting comprehensive and reproducible annotation guideline establishes a reliable methodological and data foundation for legal argument mining, computational modeling of legal reasoning, and AI-assisted legal analysis.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

This guideline proposes a systematic and operational annotation framework for representing the structure of legal argumentation in judicial decisions. Grounded in theories of legal reasoning and argumentation, the framework aims to reveal the logical organization of judicial reasoning and to provide a reliable data foundation for computational analysis. At the proposition level, the guideline distinguishes four types of propositions: general normative propositions, specific normative propositions, general factual propositions, and specific factual propositions. At the relational level, five types of relations are defined to capture argumentative structures: support, attack, joint, match, and identity. These relations represent positive and negative argumentative connections, conjunctive reasoning structures, the correspondence between legal norms and case facts, and semantic equivalence between propositions. The guideline further specifies formal representation rules and visualization conventions for both basic and nested structures, enabling consistent graphical representation of complex argumentation patterns. In addition, it establishes a standardized annotation workflow and consistency control mechanisms to ensure reproducibility and reliability of the annotated data. By providing a clear conceptual model, formal representation rules, and practical annotation procedures, this guideline offers methodological support for large-scale analysis of judicial reasoning and for future research in legal argument mining, computational modeling of legal reasoning, and AI-assisted legal analysis.

Problem

• legal argumentation
• judicial decisions
• annotation framework
• argument structure
• computational analysis

Innovation

• legal argumentation
• annotation framework
• argumentative relations
• computational legal reasoning
• judicial decision analysis