Comparer des méthodes

	Reconnaissance d'entités nommées auto-supervisée	Reconnaissance d'entités nommées (REN)
Domaine	Apprentissage profond	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2018–2019	—
Auteur d'origine	Devlin et al.; community-evolved from BERT-era self-supervised pretraining	—
Type	Sequence labeling via self-supervised pretraining + fine-tuning	NLP sequence-labelling task
Source fondatrice	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Alias	Self-supervised NER, SS-NER, label-efficient NER, pre-trained NER	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Apparentées	2	3
Résumé	Self-supervised named entity recognition (NER) combines large-scale self-supervised pretraining — such as masked language modeling — with token-level fine-tuning to identify and classify named entities in text. By learning general linguistic representations before seeing any entity labels, the model achieves strong performance even when annotated NER training data is scarce.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.