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Mining Legal Arguments in U.S. Corporate Case Law — arXiv:2609.25441

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.25441
License (as archived): CC0-1.0
Archived: 2026-10-08T14:34:28.353Z
SHA-256 of the archived text: 4be4b324cafc8943a145950a54f21bb7c5e242c56e0e54f321fb74129eafc088
Stable URL of this page: https://metraipolis.com/documento/dc_fb4fc73cf656

How to cite

Mining Legal Arguments in U.S. Corporate Case Law — arXiv:2609.25441 — arXiv (en), https://arxiv.org/abs/2609.25441 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fb4fc73cf656 (Metraipolis archive dc_fb4fc73cf656, sha256 4be4b324cafc8943a145950a54f21bb7c5e242c56e0e54f321fb74129eafc088)

BibTeX

@misc{dcfb4fc73cf656,
  title = {Mining Legal Arguments in U.S. Corporate Case Law — arXiv:2609.25441},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.25441},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fb4fc73cf656; sha256 4be4b324cafc8943a145950a54f21bb7c5e242c56e0e54f321fb74129eafc088},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.25441. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-23 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.25441 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T13:46:15.676Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).