The English Worldwide Newswire dataset, as introduced in Do "English" Named Entity Recognizers Work Well on Global Englishes? (EMNLP 2023) by Shan et. al. https://arxiv.org/abs/2404.13465

Alex Shan (azshan@cs.stanford.edu) is the correspondence author and maintainer of this repository.

This dataset is composed of ~1100 news articles from around the world, sourced from non-Western newswire. This dataset is specifically designed to exclude Western sourced texts and focuses on uncommon contexts of the English language. We encourage authors to benchmark their English NER models on this dataset to explore the efficacy of modern models on unseen contexts. Below is a detailed breakdown of article origins.

Overview of dataset

• 1075 hand-annotated English newswire articles from local sources around the world (bucketed into Asia, Africa, Latin America, the Middle East, and Indigenous Commonwealth (Oceania + Canada)).
• 700,000 tokens
• Created in collaboration with Datasaur NLP (https://datasaur.ai) and MLTwist (https://mltwist.com)
• 9 class labels: Date, Person, Location, Facility, Organization, Miscellaneous, Money, NORP, and Product. A more detailed overview of the definition for each class can be found in the appendix of the ArXiv paper.
• BIOES format: We also tag each token with its class and position, denoting whether the token is the start, intermediate, or end of a named entity.

To process the dataset, check out StanfordNLP's Stanza library which contains the dataset preparation script: https://github.com/stanfordnlp/stanza/blob/main/stanza/utils/datasets/ner/prepare_ner_dataset.py

South America: 94
	Argentina	20
	Bolivia	3
	Chile	12
	Colombia	10
	Ecuador	10
	Guyana	3
	Paraguay	13
	Peru	10
	Uruguay	5
	Venezuela	8
Central and North America: 178
	Costa Rica	20
	Cuba	15
	El Salvador	20
	Honduras	14
	Mexico	29
	Nicaragua	20
	Panama	20
	Indigenous Canadian	40
Africa: 265
	General	65
	Pan-Africa	20
	Algeria	20
	Ghana	20
	Kenya	23
	Mauritius	20
	Egypt	22
	Ethiopia	9
	Namibia	28
	South Africa	38
Asia: 347
	General	14
	China	104
	Japan	15
	India	71
	Korea	37
	Taiwan	26
	Malaysia	11
	Bangladesh	31
	Thailand	27
	Mongolia	11
Middle East: 167
	Oman	12
	Jordan	21
	Israel	20
	Iran	16
	UAE	17
	Saudi Arabia	27
	Pakistan	2
	Qatar	16
	Kuwait	36
Oceania: 48
	Indigenous Australia	28
	Indigenous New Zealand	20

Repo organization

Inside the original_articles directory, you can find the complete collection of our raw text data before the labeling process. In the procesed_annotated directory, you may find the complete collection of our annotations of the original data. Within the directory, you will find .tsv files containing BIOES-format labeled data. In the other directories, you may find the complete collection of our annotations and annotator metadata computed on the Datasaur platform. To access the labeled data, use the REVIEW subdirectory of each folder. Each line delimits a separate token that is tab-delimited between its text and corresponding label. The file names come in the form of <country>_<newswire_company>_<id>.txt.tsv. To understand where these countries are within the geographic buckets, refer to the /regions.txt file for each prefix conversion.

If you use this dataset, please use the following citation:

@inproceedings{Shan_2023,
   title={Do “English” Named Entity Recognizers Work Well on Global Englishes?},
   url={http://dx.doi.org/10.18653/v1/2023.findings-emnlp.788},
   DOI={10.18653/v1/2023.findings-emnlp.788},
   booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
   publisher={Association for Computational Linguistics},
   author={Shan, Alexander and Bauer, John and Carlson, Riley and Manning, Christopher},
   year={2023} }