Datasets:

ajesujoba
/

yoruba_gv_ner

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- expert-generated
	languages:
	- yo
	licenses:
	- Creative Commons 3-0
	multilinguality:
	- monolingual
	size_categories:
	- 200<n<1k
	source_datasets:
	- original
	task_categories:
	- structure-prediction
	task_ids:
	- named-entity-recognition
	---

	# Dataset Card for Yoruba GV NER Corpus

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)

	## Dataset Description

	- Homepage:
	- Repository: [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER)
	- Paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
	- Leaderboard:
	- Point of Contact: [David Adelani](mailto:didelani@lsv.uni-saarland.de)

	### Dataset Summary
	The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) 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](https://yo.globalvoices.org/).


	[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](https://www.aclweb.org/anthology/2020.lrec-1.335/).

	[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 ](https://creativecommons.org/licenses/by/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",
	}
	```