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

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

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}
}