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Datasets:
Linguateca
/
harem

Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Portuguese
Size:
n<1K
License:
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - pt
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: null
pretty_name: HAREM
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PESSOA
              '2': I-PESSOA
              '3': B-ORGANIZACAO
              '4': I-ORGANIZACAO
              '5': B-LOCAL
              '6': I-LOCAL
              '7': B-TEMPO
              '8': I-TEMPO
              '9': B-VALOR
              '10': I-VALOR
              '11': B-ABSTRACCAO
              '12': I-ABSTRACCAO
              '13': B-ACONTECIMENTO
              '14': I-ACONTECIMENTO
              '15': B-COISA
              '16': I-COISA
              '17': B-OBRA
              '18': I-OBRA
              '19': B-OUTRO
              '20': I-OUTRO
    splits:
      - name: test
        num_bytes: 1062714
        num_examples: 128
      - name: train
        num_bytes: 1506373
        num_examples: 121
      - name: validation
        num_bytes: 51318
        num_examples: 8
    download_size: 1887281
    dataset_size: 2620405
  - config_name: selective
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PESSOA
              '2': I-PESSOA
              '3': B-ORGANIZACAO
              '4': I-ORGANIZACAO
              '5': B-LOCAL
              '6': I-LOCAL
              '7': B-TEMPO
              '8': I-TEMPO
              '9': B-VALOR
              '10': I-VALOR
    splits:
      - name: test
        num_bytes: 1062714
        num_examples: 128
      - name: train
        num_bytes: 1506373
        num_examples: 121
      - name: validation
        num_bytes: 51318
        num_examples: 8
    download_size: 1715873
    dataset_size: 2620405

Dataset Card for HAREM

Table of Contents

Dataset Description

Dataset Summary

The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts, from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set, a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event, Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date).

It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type". The dataset version processed here ONLY USE the "Category" level of the original dataset.

[1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

Portuguese

Dataset Structure

Data Instances 

{
  "id": "HAREM-871-07800",
  "ner_tags": [3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4,
  ],
  "tokens": [
    "Abraço", "Página", "Principal", "ASSOCIAÇÃO", "DE", "APOIO", "A", "PESSOAS", "COM", "VIH", "/", "SIDA"
  ]
}

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-PESSOA", "I-PESSOA", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-LOCAL", "I-LOCAL", "B-TEMPO", "I-TEMPO", "B-VALOR", "I-VALOR", "B-ABSTRACCAO", "I-ABSTRACCAO", "B-ACONTECIMENTO", "I-ACONTECIMENTO", "B-COISA", "I-COISA", "B-OBRA", "I-OBRA", "B-OUTRO", "I-OUTRO"

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 for each of the two versions (default and selective). The split sizes are as follow:

Train Val Test
121 8 128

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{santos2006harem,
  title={Harem: An advanced ner evaluation contest for portuguese},
  author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
  booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)},
  year={2006}
}

Contributions

Thanks to @jonatasgrosman for adding this dataset.