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Similar Phrases for Cause of Actions of Civil Cases

Ho-Chien Huang, Chao-Lin Liu

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.

Similar Phrases for Cause of Actions of Civil Cases

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.

Paper Structure

This paper contains 13 sections, 8 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Data Source
  4. Measuring the Similarity between COAs
  5. Methodology
  6. Result of Implementing Similarity Measurement
  7. Ensemble and Identiyfing Clusters of Similar COAs via Social Network Analysis
  8. Determining the Similarity Threshold
  9. Embedding and Clustering
  10. Utility Function
  11. Result and Threshold Determination
  12. Empirical Observations
  13. Discussion and Concluding Remarks

Figures (4)

  • Figure 1: Distributions of the number of cited articles in the sampled cases
  • Figure 2: A cluster drawn by Gehpi.
  • Figure 3: Distribution of similar COA pairs and their ensemble model rankings
  • Figure 4: Social network representation of the similarity relationships between COAs