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Dataset Card for leNER-br

Dataset Summary

LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The language supported is Portuguese.

Dataset Structure

Data Instances

An example from the dataset looks as follows:

{
  "id": "0",
  "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0],
  "tokens": [
    "EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"]
}

Data Fields

id: id of the sample
tokens: the tokens of the example text
ner_tags: the NER tags of each token

The NER tags correspond to this list:

"O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA"

The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word.

Data Splits

The data is split into train, validation and test set. The split sizes are as follow:

Train	Val	Test
7828	1177	1390

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

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

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{luz_etal_propor2018,
      author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
      Renato R. R. {de Oliveira} and Matheus Stauffer and
      Samuel Couto and Paulo Bermejo},
      title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
      booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
      publisher = {Springer},
      series = {Lecture Notes on Computer Science ({LNCS})},
      pages = {313--323},
      year = {2018},
      month = {September 24-26},
      address = {Canela, RS, Brazil},	  
      doi = {10.1007/978-3-319-99722-3_32},
      url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
}

Contributions

Thanks to @jonatasgrosman for adding this dataset.

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leNER-BR: Long Form Question Answering

Point of Contact:

Pedro H. Luz de Araujo

Models trained or fine-tuned on peluz/lener_br