Mining Legal Arguments in U.S. Corporate Case Law

2026-09-21

AI Summary

本文构建了一个专家标注的美国联邦税务案例数据集，通过分类、检索和论证完成等方法支持法律论证挖掘。

Abstract

Legal argument mining supports passage classification, retrieval, and argument completion. This work introduces an expert-annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations under I.R.C. §368. To our knowledge, it is the first expert-annotated, tree-structured argument corpus for this domain. Explicit spans receive one of five functional labels: Rule, Analysis, Conclusion, Background Facts, and Procedural History. Rule, Analysis, and Conclusion spans can be linked into directed support trees, while Background Facts and Procedural History serve a contextual function. The corpus provides span-based, sentence-based, flat, and tree-structured representations. Agreement analysis shows that functional node labels are more reliable than directed support edges and implicit intermediate conclusions. Directed-path agreement is stronger than direct-edge agreement, which indicates that broad reachability is more stable than exact local decomposition. Classification experiments show that functional labels are learnable under case-disjoint evaluation. Retrieval experiments show that supervised fine-tuning improves within-case retrieval. However, cross-case generalization remains weak. The dataset supports legal passage classification and provides a conservative benchmark for structured argument mining in U.S. federal tax case law.

Problem

• Legal Argument Mining
• U.S. Federal Tax Case Law
• Corporate Reorganizations
• Expert-Annotated Dataset
• Argument Corpus

Innovation

• expert-annotated
• tree-structured argument corpus
• functional labels
• case-disjoint evaluation
• supervised fine-tuning