metadata

license: cc-by-nd-4.0
viewer: true
task_categories:
  - token-classification
tags:
  - legal
pretty_name: Multilingual Negation Scope Resolution
size_categories:
  - 1K<n<10K

Dataset Card for MultiLegalNeg

Dataset Summary

This dataset consists of German, French, and Italian court documents annotated for negation cues and negation scopes. It also includes a reformated version of ConanDoyle-neg ( Morante and Blanco. 2012), SFU Review (Konstantinova et al. 2012), BioScope (Szarvas et al. 2008) and Dalloux (Dalloux et al. 2020).

Languages

Language	Subset	Number of sentences	Negated sentences
French	fr	1059	382
Italian	it	1001	418
German(Germany)	de(DE)	1068	1098
German (Switzerland)	de(CH)	206	208
English	SFU Review	17672	3528
English	BioScope	14700	2095
English	ConanDoyle-neg	5714	5714
French	Dalloux	11032	1817

Dataset Structure

Data Fields

text (string): full sentence
spans (list): list of annotated cues and scopes
- start (int): offset of the beginning of the annotation
- end (int): offset of the end of the annotation
- token_start(int): id of the first token in the annotation
- token_end(int): id of the last token in the annotation
- label (string): CUE or SCOPE
tokens (list): list of tokens in the sentence
- text (string): token text
- start (int): offset of the first character
- end (int): offset of the last character
- id (int): token id
- ws (boolean): indicates if the token is followed by a white space

Data Splits

For each subset a train (70%), test (20%), and validation (10%) split is available.

How to use this dataset

To load all data use 'all_all', or specify which dataset to load as the second argument. The available configurations are 'de', 'fr', 'it', 'swiss', 'fr_dalloux', 'fr_all', 'en_bioscope', 'en_sherlock', 'en_sfu', 'en_all', 'all_all'

from datasets import load_dataset

dataset = load_dataset("rcds/MultiLegalNeg", "all_all")

dataset

DatasetDict({
    train: Dataset({
        features: ['text', 'spans', 'tokens'],
        num_rows: 26440
    })
    test: Dataset({
        features: ['text', 'spans', 'tokens'],
        num_rows: 7593
    })
    validation: Dataset({
        features: ['text', 'spans', 'tokens'],
        num_rows: 4053
    })
})

Source Data

Subset	Source
fr	Niklaus et al. 2021, Niklaus et al. 2023
it	Niklaus et al. 2021, Niklaus et al. 2023
de(DE)	Glaser et al. 2021
de(CH)	Niklaus et al. 2021
SFU Review	Konstantinova et al. 2012
BioScope	Szarvas et al. 2008
ConanDoyle-neg	Morante and Blanco. 2012
Dalloux	Dalloux et al. 2020

Annotations

The data is annotated for negation cues and their scopes. Annotation guidelines are available here

Annotation process

Each language was annotated by one native speaking annotator and follows strict annotation guidelines

Citation Information

Please cite the following preprint:

@misc{christen2023resolving,
      title={Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents}, 
      author={Ramona Christen and Anastassia Shaitarova and Matthias Stürmer and Joel Niklaus},
      year={2023},
      eprint={2309.08695},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}