摘要：法律论证挖掘可支持段落分类、检索和论证补全。本研究推出了一个由专家标注的数据集，包含 42 份涉及《美国国内税收法典》（I.R.C.）§368 公司重组规定的美国联邦税务判决意见书。据我们所知，这是该领域首个由专家标注、采用树状结构的论证语料库。显式文本片段被赋予五种功能标签之一：规则（Rule）、分析（Analysis）、结论（Conclusion）、背景事实（Background Facts）和程序历史（Procedural History）。规则、分析和结论片段可被连接为有向支持树，而背景事实和程序历史则发挥语境功能。该语料库提供基于片段、基于句子、扁平化和树状结构的多种表示形式。一致性分析表明，功能节点标签的可靠性高于有向支持边和隐含的中间结论。有向路径的一致性强于直接边的一致性，这表明广义可达性比精确的局部结构分解更稳定。分类实验显示，在按案件隔离的评估条件下，功能标签是可学习的。检索实验显示，监督式微调可提升同一案件内的检索效果。然而，跨案件泛化能力仍然较弱。该数据集支持法律段落分类，并为美国联邦税务案例法中的结构化论证挖掘提供了一个相对保守的基准。

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.