Datasets:
annotations_creators:
- found
- other
language_creators:
- found
language:
- en
- fr
- hu
- it
- nb
- nl
- pl
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: EXCEPTIUS Corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
Dataset Card for EXCEPTIUS Corpus
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://exceptius.com/
- Repository: https://github.com/tommasoc80/COVID19_emergency_event
- Paper: Tziafas, G., de Saint-Phalle, E., de Vries, W., Egger, C., & Caselli, T. (2021). A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19. Proceedings of the Natural Legal Language Processing Workshop 2021, 46–62. https://doi.org/10.18653/v1/2021.nllp-1.5
- Leaderboard:
- Point of Contact: Joel Niklaus
Dataset Summary
This dataset presents a new corpus of legislative documents from 8 European countries (Beglium, France, Hunary, Italy, Netherlands, Norway, Poland, UK) in 7 languages (Dutch, English, French, Hungarian, Italian, Norwegian Bokmål, Polish) manually annotated for exceptional measures against COVID-19. The annotation was done on the sentence level.
Supported Tasks and Leaderboards
The dataset can be used for multi-label text classification tasks.
Languages
Dutch, English, French, Hungarian, Italian, Norwegian Bokmål, Polish
Dataset Structure
Data Instances
The file format is jsonl and three data splits are present (train, validation and test).
Data Fields
The jsonl files have the following basic columns:
language: The language of the sentence (set based on the country)country: The country of the sentencetext: Sentence that has been annotated
The documents have been annotated with 8 labels, each label representing a specific measurement against COVID-19. Each label is represented by one boolean field in the jsonl file. The labels, i.e. the specific measure classes, are:
event1: State of Emergencyevent2: Restrictions of fundamental rights and civil libertiesevent3: Restrictions of daily libertiesevent4: Closures / lockdownevent5: Suspension of international cooperation and commitmentsevent6: Police mobilizationevent7: Army mobilizationevent8: Government oversightall_events: an aggregate column containing all applicable events combined
Data Splits
All annotated sentences combined have the following split:
- train: 3312 (80%)
- dev: 418 (10%)
- test: 418 (10%)
The splits have been performed on each country and have later been merged. Therefore, each split contains sentences from each country.
The following label distribution shows the number of occurrences per label per split. total occurrences sums up the previous rows (total number of events per split). split size is the number of sentences per split.
| Event | train | validation | test |
|---|---|---|---|
| event1 | 383 | 54 | 47 |
| event2 | 253 | 39 | 42 |
| event3 | 412 | 70 | 62 |
| event4 | 617 | 75 | 93 |
| event5 | 52 | 4 | 6 |
| event6 | 15 | 2 | 1 |
| event7 | 45 | 4 | 5 |
| event8 | 146 | 21 | 19 |
| total occurrences | 1923 | 269 | 275 |
| split size | 3312 | 418 | 418 |
Dataset Creation
Curation Rationale
"Investigate the potential of multilingual pretrained language models in order to facilitate the analysis, exploration, and comparison of legal texts on COVID-19 exceptional measures" (Tziafas et al., 2021)
Source Data
Initial Data Collection and Normalization
“The corpus collection process has been overseen by four political science experts working in partnership with national legal experts. All documents were retrieved from official governmental websites that publish legal acts. The identification of the relevant documents has been done by means of 4 keywords (i.e., “COVID”, “COVID-19”, “Coronavirus” and “Health emergency”). For each language, the corresponding language specific keywords were used. In this initial phase, we focus on a sample of 19 EEA countries on measures adopted at the national level. To do so, we identify publicly available links to relevant documents 2 plus UK and Switzerland. We could not find corresponding documents for two countries of the EEA (i.e., Bulgaria and Greece). All documents have been collected either by manually downloading them or by automatic scraping. For countries with more than one official language (e.g., Switzerland), legal acts were collected in all available languages.”(Tziafas et al., 2021)
Who are the source language producers?
Politicians and legal experts have been involved in producing the language material.
Annotations
Annotation process
"A subset of 281 documents in eight languages has been selected for manual annotation. The annotation of the exceptional measures applies at sentence-level. The sample is based on the French, Polish, Dutch, English, Hungarian, Belgian, Italian, and Norwegian sub-corpora. Annotators were allowed to assign as many subclasses as they consider relevant to each sentence, but with a total of eight main classes of exceptional measures. Sentences can potentially entail multiple exceptional classes, making this a multi-label annotation task. The annotation process results in eight binary annotations per sentence, with 0 if the specific class is not identified within the sentence and 1 if it is. The annotation has been conducted by three experts in political science working under the supervision of the project’s Scientific Board. Since the annotators are not fluent in all languages and due to the impossibility of recruiting expert native speakers, some documents need to be translated into English to be manually annotated. No inter-annotator agreement study has been conducted in this initial phase. We intend to remedy this limitation in the project’s next development cycle. However, during the annotation phase, annotators met on a weekly basis to discuss ambiguous cases and the guidelines. Annotators are encouraged to propose new classes or subclasses. For a new (sub)class to be accepted, the measure should have been independently identified by the majority of the annotators. In this phase, no new classes were proposed." (Tziafas et al., 2021)
Who are the annotators?
"The annotation has been conducted by three experts in political science working under the supervision of the project’s Scientific Board." (Tziafas et al., 2021)
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script convert_to_hf_dataset.py in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
Additional Information
Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section Citation Information. Additional changes were made by Joel Niklaus (Email; Github) and Veton Matoshi (Email; Github).
Licensing Information
Creative Commons Zero v1.0 Universal
Citation Information
@inproceedings{tziafas-etal-2021-multilingual,
title = "A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19",
author = "Tziafas, Georgios and
de Saint-Phalle, Eugenie and
de Vries, Wietse and
Egger, Clara and
Caselli, Tommaso",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.5",
pages = "46--62",
}
Contributions
Thanks to @JoelNiklaus and @kapllan for adding this dataset.