Summary of Discovering Significant Topics From Legal Decisions with Selective Inference, by Jerrold Soh
Discovering Significant Topics from Legal Decisions with Selective Inference
by Jerrold Soh
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes an automated method to identify significant topics in legal decision texts, leveraging topic models and penalized regressions. The approach passes features through post-selection significance tests to determine correlated outcomes, providing insights into case topics. The pipeline yields interpretable topic-word distributions and case-topic weights for representative cases. The study demonstrates the effectiveness on two datasets: domain name disputes and European Court of Human Rights violation cases. Evaluations include latent semantic analysis-based topic models and language model embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand important ideas in legal decisions is developed by this research. It uses special computer algorithms to find topics that are connected to the outcome of a case. The method gives insights into what words are most related to each topic, making it easier to understand the important ideas. This approach was tested on two types of cases: domain name disputes and European Court of Human Rights violations. The results show that the method can be useful in many other legal analysis tasks. |
Keywords
» Artificial intelligence » Language model
