发表机构
University of Delhi; IBM Research（德里大学; IBM 研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究法律先例检索问题，提出PRecG管道，通过基于句子修辞角色分解文档、构建知识图、学习聚合实体上下文表示等步骤，分层学习法律判决对的表示来计算相似度，经实验验证其有效性。
AI 中文摘要
法律先例检索是法律案件准备、规划、诉讼策略和法律研究中的一项基本任务。当前自动先例检索方法将法律文件映射到低维语义空间并基于表示的接近度计算相似度，忽略了法律技术细节的修辞组织，从而忽略细微法律含义，无法区分法律实体和概念基于文档中修辞角色的上下文意义。为解决这一不足，我们提出PRecG管道，通过分层学习法律判决对的表示来计算相似度。首先基于句子修辞角色将文档分解为不同语义单元，为每个修辞段构建知识图以捕获其中法律实体及其关系，学习并聚合实体的上下文表示以获得段级嵌入，进一步整合这些嵌入以生成统一的文档级表示，最后计算文档对之间的语义相似度。我们在印度法律基准数据集上进行广泛实验验证了该方法的性能，并与现有基线进行比较以证明其有效性。
英文摘要
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. 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.
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