# An explainable approach to detect case law on housing and eviction issues within the HUDOC database

arXiv: https://arxiv.org/abs/2410.02978 · HTML: https://mlsanity.com/paper/2410.02978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Visualisation and knowledge discovery from interpretable models](https://mlsanity.com/paper/2005.03632/) (2020)
- [Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques](https://mlsanity.com/paper/2201.01604/) (2022)
- [Automatic Text Summarization of Legal Cases: A Hybrid Approach](https://mlsanity.com/paper/1908.09119/) (2019)
- [Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets](https://mlsanity.com/paper/2206.02056/) (2022)
- [Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family life](https://mlsanity.com/paper/2401.16429/) (2024)
- [Generalized Relevance Learning Grassmann Quantization](https://mlsanity.com/paper/2403.09183/) (2024)
- [A prototype-based model for set classification](https://mlsanity.com/paper/2408.13720/) (2024)

## Abstract (arXiv)

Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.