Similar Phrases for Cause of Actions of Civil Cases

TL;DR

This paper proposes a novel method using embedding, clustering, and social network analysis to identify and analyze similarities between Causes of Action in Taiwanese civil cases, improving legal case filtering.

Contribution

It introduces an ensemble similarity model and social network analysis to standardize and cluster COAs, addressing the lack of labeling consistency in Taiwanese civil law.

Findings

Enhanced identification of related COAs through clustering

Improved filtering of legal cases using similarity measures

Revealed hidden connections between COAs

Abstract

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.