Papers › Do "English" Named Entity Recognizers Work Well on Global Englishes?

Do "English" Named Entity Recognizers Work Well on Global Englishes?

20 Apr 2024arXiv:2404.13465archive 2025-07-28

Alexander Shan, John Bauer, Riley Carlson, Christopher Manning

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally. To test this, we build a newswire dataset, the Worldwide English NER Dataset, to analyze NER model performance on low-resource English variants from around the world. We test widely used NER toolkits and transformer models, including models using the pre-trained contextual models RoBERTa and ELECTRA, on three datasets: a commonly used British English newswire dataset, CoNLL 2003, a more American focused dataset OntoNotes, and our global dataset. All models trained on the CoNLL or OntoNotes datasets experienced significant performance drops-over 10 F1 in some cases-when tested on the Worldwide English dataset. Upon examination of region-specific errors, we observe the greatest performance drops for Oceania and Africa, while Asia and the Middle East had comparatively strong performance. Lastly, we find that a combined model trained on the Worldwide dataset and either CoNLL or OntoNotes lost only 1-2 F1 on both test sets.

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juand-r/entity-recognition-datasets officialmentioned in papermentioned on GitHubtorchMIT report

stanfordnlp/en-worldwide-newswire officialmentioned in papermentioned on GitHub report

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NER Named Entity Recognition Named Entity Recognition (NER)named-entity-recognition

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Adam Attention Attention Dropout BERT Dense Connections Dropout ELECTRA Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection RoBERTa Softmax Weight Decay WordPiece

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