arXiv:2607. 09094v1 Announce Type: cross Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research.
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The paper introduces an expert‑annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations under I.R.C. §368, marking the first tree‑structured argument corpus in this domain. Each legal passage is labeled with one of five functional categories—Rule, Analysis, Conclusion, Background Facts, and Procedural History—and can be linked into directed support trees. Experiments demonstrate that functional labels are learnable and that supervised fine‑tuning improves within‑case retrieval, though cross‑case generalization remains weak.
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...
Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory language. In this paper, we propose GCSR, a generative statute retrieval framework that reformulates statute retrieval as a sequence generation problem and internalizes statutory knowledge into a generative model.
arXiv:2608.22446v1 Announce Type: new Abstract: Metaphors are figurative use of words for conceptual mapping. Metaphor detection in the legal context has been crucial as metaphors are persuasive juri...
arXiv:2606. 06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing largely to the absence of expert-annotated corpora.
LexIssue introduces a benchmark for identifying disputed legal issues in Chinese civil litigation, comprising 430 real‑world cases and 1,303 expert‑annotated issues. The dataset is built around a hierarchical schema that links free‑form issue descriptions to structured legal categories, enabling two complementary tasks: issue generation and issue classification. A retrieval‑augmented knowledge base covering 27 causes of action and 441 issue entries is provided, and experiments show that incorporating this knowledge consistently improves model performance on the tasks.