Comparer des méthodes

	Apprentissage à peu d'exemples	Reconnaissance d'entités nommées (REN)
Domaine	Apprentissage automatique	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2011–2017	—
Auteur d'origine	Lake, B. M.; Vinyals, O.; Finn, C. et al.	—
Type	Meta-learning / low-data learning paradigm	NLP sequence-labelling task
Source fondatrice	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Alias	FSL, low-shot learning, k-shot learning, meta-learning for few examples	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Apparentées	4	3
Résumé	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.	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.