Summary of An Explainable Approach to Detect Case Law on Housing and Eviction Issues Within the Hudoc Database, by Mohammad Mohammadi et al.
An explainable approach to detect case law on housing and eviction issues within the HUDOC database
by Mohammad Mohammadi, Martijn Wieling, Michel Vols
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper addresses the challenge of extracting detailed insights from human rights case law, specifically from the European Court of Human Rights (ECtHR). The study focuses on the HUDOC database, which provides access to ECtHR case law and metadata. However, this metadata often lacks essential information, such as specific issues addressed in a case. To address this limitation, an automated solution is proposed to analyze the large dataset containing over 40,000 cases. The paper aims to develop a method for extracting detailed insights from human rights case law, enabling more effective understanding and application of ECtHR rulings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand important human rights cases that shape our understanding of what we need to live safely and comfortably, like having a place to call home. The problem is that it’s hard to get detailed information about each case from the database, so an automated way to analyze all these cases is needed. |
