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Datasets:
ajesujoba
/
yoruba_gv_ner

Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Yoruba
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
Dataset card Files Community
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - yo
license:
  - cc-by-3.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: null
pretty_name: Yoruba GV NER Corpus
dataset_info:
  features:
    - name: id
      dtype: string
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-ORG
            '4': I-ORG
            '5': B-LOC
            '6': I-LOC
            '7': B-DATE
            '8': I-DATE
  config_name: yoruba_gv_ner
  splits:
    - name: train
      num_bytes: 358885
      num_examples: 817
    - name: validation
      num_bytes: 50161
      num_examples: 117
    - name: test
      num_bytes: 96518
      num_examples: 237
  download_size: 254347
  dataset_size: 505564

Dataset Card for Yoruba GV NER Corpus

Table of Contents

Dataset Description

Dataset Summary

The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the Global Voices news corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The language supported is Yorùbá.

Dataset Structure

Data Instances

A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-LOC, 0, 0, 0, 0], 'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe'] }

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-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",

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. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.

Data Splits

Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens)

Dataset Creation

Curation Rationale

The data was created to help introduce resources to new language - Yorùbá.

[More Information Needed]

Source Data

Initial Data Collection and Normalization

The dataset is based on the news domain and was crawled from Global Voices Yorùbá news.

[More Information Needed]

Who are the source language producers?

The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá [More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

The data was annotated by Jesujoba Alabi and David Adelani for the paper: Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi.

[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

The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .

Licensing Information

The data is under the Creative Commons Attribution 3.0

Citation Information 

@inproceedings{alabi-etal-2020-massive,
    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
    author = "Alabi, Jesujoba  and
      Amponsah-Kaakyire, Kwabena  and
      Adelani, David  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
    pages = "2754--2762",
    language = "English",
    ISBN = "979-10-95546-34-4",
}

Contributions

Thanks to @dadelani for adding this dataset.