通过分段匹配法律推理，提升判例检索准确性。
Section-Weighted Hybrid Approach for Legal Case Retrieval
- 分段处理判决书，按事实、争议、裁决等部分进行匹配。
- 融合词法与语义搜索，在基准上超越主流方法。
- 输出带立场标签的精准段落和理由，适合法律实务使用。
寻找真正类比的判例需超越表面词汇重叠，捕捉法律推理本质。本文提出一种两阶段、分段感知的判例检索框架：第一阶段利用确定性大语言模型（LLM）离线将原始判决文本切分为事实、争议、裁决和推理四部分；第二阶段在细粒度层面进行同类项比较（如查询推理对候选推理）。为解决词法得分与余弦相似度量纲不一问题，采用查询级Z-score标准化后，结合学习得到的段落权重进行信号融合。系统对前若干结果返回相关段落文本、简明且有依据的理由说明及当事人立场标签。在司法辖区规模基准上评估，该方法持续优于强基线模型，同时保持高候选覆盖率。
原文摘要 · Abstract (English)
Finding truly analogous precedents requires capturing legal reasoning beyond surface word overlap. We present a two-stage, section-aware framework for legal case retrieval that first segments raw judgments into facts, issues, decision, and reasoning using a deterministic large language model (LLM) offline. In Stage 1, we combine parallel lexical (BM25) and semantic (dense ANN) whole-document searches via Reciprocal Rank Fusion (RRF) to form a high-recall candidate pool. In Stage 2, we perform fine-grained, like-for-like comparisons (e.g., query reasoning vs. candidate reasoning). To address the scale mismatch between unbounded lexical scores and cosine similarities, we apply query-wise Z-score normalization before aggregating signals with learned section weights. For the top results, the system returns the relevant section text with a concise, grounded rationale and party-stance labels. We evaluate on a jurisdiction-scale benchmark, demonstrating consistent gains over strong lexical and neural baselines while maintaining high candidate coverage
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