PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation

PRecG：基于图神经网络与修辞角色分割的法律先例检索

Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research. Current approaches for automatic precedent retrieval map legal documents to a low-dimensional semantic space and compute similarity based on the proximity of their representations. These approaches treat legal documents as monolithic texts, ignoring the rhetorical organization of the legal technicalities. Ergo, they overlook nuanced legal meanings and fail to distinguish the contextual significance of legal entities and concepts that vary based on their rhetorical roles within the document.

摘要： 法律先例检索是法律案件准备、规划、诉讼策略及法律研究中的一项基础性任务。目前的自动先例检索方法通常将法律文档映射到低维语义空间，并根据其表示的邻近度来计算相似性。这些方法将法律文档视为单一的整体文本，忽略了法律技术细节中的修辞组织结构。因此，它们往往会忽略细微的法律含义，且无法区分法律实体和概念在文档中因修辞角色不同而产生的语境意义差异。

To address this insufficiency, we propose the PRecG pipeline that computes the similarity between pairs of legal judgments by hierarchically learning their representations. The process begins by decomposing each document into distinct semantic units (segments) based on the rhetorical roles of sentences. For each rhetorical segment, a knowledge graph is constructed to capture the legal entities and their relationships within the segment. Contextual representations of the entities are then learned and aggregated to derive segment-level embeddings. These embeddings are further integrated to produce a unified document-level representation, and finally, the semantic similarity between a pair of documents is computed. We validate the performance of the proposed approach through extensive experiments on a benchmark Indian legal dataset, comparing it against state-of-the-art baselines to demonstrate its effectiveness.

为了解决这一不足，我们提出了 PRecG 流水线，通过分层学习法律判决书的表示来计算其两两之间的相似度。该过程首先根据句子的修辞角色将每份文档分解为不同的语义单元（片段）。针对每个修辞片段，构建知识图谱以捕获片段内的法律实体及其关系。随后，学习并聚合实体的语境表示，从而导出片段级的嵌入。这些嵌入进一步整合以生成统一的文档级表示，最终计算出一对文档之间的语义相似度。我们通过在印度法律基准数据集上的大量实验验证了该方法的性能，并将其与最先进的基准模型进行了对比，证明了其有效性。