用相似度分析找出民事案件要件间的隐藏关联。
Similar Phrases for Cause of Actions of Civil Cases
- 基于引用法条计算要件相似度，结合聚类与图分析。
- 通过集成模型和相关系数提升要件匹配准确率。
- 适合法律研究者探索案件间深层联系。
在台湾司法体系中，要件（Cause of Actions, COAs）是识别相关判例的关键。然而，缺乏标准化的COA标注使得传统方法难以有效筛选案件。本研究利用嵌入与聚类技术，基于引用法条分析COAs之间的相似性，采用Dice系数和皮尔逊相关系数等多重相似度度量方式。通过集成模型融合排名结果，并运用社会网络分析识别出相关联的COA集群。该方法揭示了要件间的隐含关联，为民事法以外的法律研究提供了潜在应用价值。
原文摘要 · Abstract (English)
In the Taiwanese judicial system, Cause of Actions (COAs) are essential for identifying relevant legal judgments. However, the lack of standardized COA labeling creates challenges in filtering cases using basic methods. This research addresses this issue by leveraging embedding and clustering techniques to analyze the similarity between COAs based on cited legal articles. The study implements various similarity measures, including Dice coefficient and Pearson's correlation coefficient. An ensemble model combines rankings, and social network analysis identifies clusters of related COAs. This approach enhances legal analysis by revealing inconspicuous connections between COAs, offering potential applications in legal research beyond civil law.
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