| | --- |
| | 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} |
| | } |
| | ``` |
