Summary of Similar Phrases For Cause Of Actions Of Civil Cases, by Ho-chien Huang and Chao-lin Liu
Similar Phrases for Cause of Actions of Civil Cases
by Ho-Chien Huang, Chao-Lin Liu
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research utilizes embedding and clustering techniques to analyze the similarity between Cause of Actions (COAs) based on cited legal articles in the Taiwanese judicial system. This aims to address the lack of standardized COA labeling, which hinders filtering cases using basic methods. The study implements various similarity measures, including Dice coefficient and Pearson’s correlation coefficient, and employs an ensemble model combining rankings. Additionally, social network analysis identifies clusters of related COAs, enhancing legal analysis by revealing inconspicuous connections between COAs with potential applications beyond civil law. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make it easier to find similar court cases in Taiwan. Right now, it’s hard to search for relevant judgments because there is no standard way to label the reasons why a case was decided (called Cause of Actions or COAs). The researchers used special computer techniques called embedding and clustering to analyze how similar different COAs are based on which legal articles they cite. They tried out different ways to measure similarity, like comparing lists of articles or counting how often certain words appear. Then, they combined these rankings into one score using an ensemble model. By looking at the connections between similar COAs, this approach can help legal researchers find new relationships and patterns in court cases. |
Keywords
» Artificial intelligence » Clustering » Embedding » Ensemble model
