Similar Phrases for Cause of Actions of Civil Cases
This addresses filtering challenges for legal professionals in civil cases, but it is incremental as it applies existing methods to a specific domain.
The research tackled the problem of non-standardized Cause of Actions (COAs) in the Taiwanese judicial system by using embedding and clustering techniques to analyze similarity based on cited legal articles, revealing inconspicuous connections between COAs.
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.