Datasets:
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annotations_creators:
- no-annotation
language_creators:
- found
language:
- it
- fr
- es
- en
- de
license: cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
- sentence-similarity
task_ids:
- semantic-search
- document-retrieval
pretty_name: European National Implementing Measures Dataset (ENIMD)
tags:
- legal
- european-union
- harmonization
- legislation
---
# ENIMD: European National Implementing Measures Dataset
**ENIMD** is a large-scale multilingual dataset designed for **legal semantic search** and **harmonization analysis**. It pairs **European Directives (EUDs)** with their corresponding **National Implementing Measures (NIMs)** across five Member States.
The dataset enables the training of models to automatically identify national laws that implement EU directives, distinguishing them from domestic legislation that does not.
## 📄 Paper
**Pairing EU directives and their national implementing measures: A dataset for semantic search**
*Roger Ferrod, Denys Amore Bondarenko, Davide Audrito, Giovanni Siragusa*
Published in **Computer Law & Security Review**, Volume 51, 2023.
[**Read the Paper**](https://doi.org/10.1016/j.clsr.2023.105862) | [**GitHub Repository**](https://github.com/rogerferrod/ENIMD)
## 💾 Dataset Structure
The dataset is organized into three components, catering to different research needs:
### 1. `ML-dataset`
A shuffled, machine-learning-ready collection of articles split into **Train** and **Test** sets.
* **Content:** Pairs of EUD articles (Queries) and National Law articles (Documents).
* **Labels:** Includes `positive` examples (NIMs) and `negative` examples (irrelevant national laws).
* **Structure:** Articles are labeled with the CELEX number, country code and transposition hash.
* **Preprocessing:** Filtered using an IDF-based method to remove boilerplate text (e.g., entry into force dates, financial clauses).
### 2. `filtered`
The parsed collection of articles where irrelevant/boilerplate provisions have been removed using the method described in the paper. Useful for analysis without the noise of administrative clauses.
### 3. `raw`
The full parsed collection of Directives and National Laws in their original structure (articles/paragraphs), without any filtering.
## 📊 Statistics
The dataset covers legislation from five EU Member States:
| Country | Language | EUD Articles (Queries) | National Corpus Articles |
|:----------| :--- |:-----------------------|:-------------------------|
| Italy | Italian | 11,514 | 135,221 |
| France | French | 11,386 | 236,762 |
| Spain | Spanish | 11,249 | 209,795 |
| Ireland | English | 11,344 | 157,601 |
| Austria | German | 11,837 | 199,781 |
| **Total** | **Multilingual** | **57,330** | **939,160** |
* **Total Directives:** 906
* **Total National Documents:** 9,016
* **Ratio:** ~88% of the national corpus consists of "irrelevant" laws (negative examples), providing a realistic retrieval challenge.
## 💻 Usage
You can load the dataset using the Hugging Face `datasets` library.
```python
from datasets import load_dataset
# Load the ML-ready dataset
dataset = load_dataset("rogerferrod/ENIMD", data_dir="ML-dataset")
# Example: Inspect the first training example
print(dataset['train'][0])
```
## ⚖️ Legal Harmonization Task
The primary task is Semantic Search / Retrieval:
1. Query: An article from an EU Directive.
2. Target: The specific article(s) in National Law that implement that directive.
3. Challenge: The model must retrieve the correct implementation from a pool of ~900k national articles, most of which are unrelated.
## 📚 Citation
If you use this dataset in your research, please cite the original paper:
```bibtex
@article{FERROD2023105862,
title = {Pairing EU directives and their national implementing measures: A dataset for semantic search},
journal = {Computer Law & Security Review},
volume = {51},
pages = {105862},
year = {2023},
issn = {2212-473X},
doi = {https://doi.org/10.1016/j.clsr.2023.105862},
url = {https://www.sciencedirect.com/science/article/pii/S0267364923000729},
author = {Roger Ferrod and Denys Amore Bondarenko and Davide Audrito and Giovanni Siragusa}
}
``` |