Dataset:
eurlex

Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: cc-by-sa-4.0
Language Creators: found
Annotations Creators: found
Source Datasets: original

Dataset Card for the EUR-Lex dataset

Dataset Summary

EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old. EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones:

  • the header, which includes the title and name of the legal body enforcing the legal act;
  • the recitals, which are legal background references; and
  • the main body, usually organized in articles.

Labeling / Annotation

All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/). While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.

Supported Tasks and Leaderboards

The dataset supports:

Multi-label Text Classification: Given the text of a document, a model predicts the relevant EUROVOC concepts.

Few-shot and Zero-shot learning: As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.

Languages

All documents are written in English.

Dataset Structure

Data Instances

{
  "celex_id": "31979D0509", 
  "title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain", 
  "text": "COUNCIL DECISION  of 24 May 1979  on financial aid from the Community for the eradication of African swine fever in Spain  (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,", 
  "eurovoc_concepts": ["192", "2356", "2560", "862", "863"]
}

Data Fields

The following data fields are provided for documents (train, dev, test):

celex_id: (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.
title: (str) The title of the document.
text: (str) The full content of each document, which is represented by its header, recitals and main_body.
eurovoc_concepts: (List[str]) The relevant EUROVOC concepts (labels).

If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl

import json
with open('./eurovoc_concepts.jsonl') as jsonl_file:
    eurovoc_concepts =  {json.loads(concept) for concept in jsonl_file.readlines()}

Data Splits

Split No of Documents Avg. words Avg. labels
Train 45,000 729 5
Development 6,000 714 5
Test 6,000 725 5

Dataset Creation

Curation Rationale

The dataset was curated by Chalkidis et al. (2019).
The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).

Source Data

Initial Data Collection and Normalization

The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format. The documents were downloaded from EUR-Lex portal in HTML format. The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

Who are the annotators?

Publications Office of EU (https://publications.europa.eu/en)

Personal and Sensitive Information

The dataset does not include personal or sensitive information.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Chalkidis et al. (2019)

Licensing Information

© European Union, 1998-2021

The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.

The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.

Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html

Citation Information

Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos. Large-Scale Multi-Label Text Classification on EU Legislation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019

@inproceedings{chalkidis-etal-2019-large,
    title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
    author = "Chalkidis, Ilias  and Fergadiotis, Manos  and Malakasiotis, Prodromos  and Androutsopoulos, Ion",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1636",
    doi = "10.18653/v1/P19-1636",
    pages = "6314--6322"
}

Contributions

Thanks to @iliaschalkidis for adding this dataset.

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