Comparer des méthodes

	Reconnaissance d'entités nommées explicable	Reconnaissance d'entités nommées (REN)
Domaine	Apprentissage profond	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2018–2020	—
Auteur d'origine	Community-driven (NLP + XAI research)	—
Type	Interpretability-augmented sequence labeling	NLP sequence-labelling task
Source fondatrice	Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL-IJCNLP), pp. 447–459.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Alias	XAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NER	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Apparentées	6	3
Résumé	Explainable Named Entity Recognition (XAI-NER) combines a standard NER model — typically a BERT-based or BiLSTM-CRF sequence labeler — with post-hoc or intrinsic explainability techniques such as LIME, SHAP, attention visualization, or gradient-based saliency to reveal why each token was assigned a particular entity label. This transparency is essential in high-stakes domains like clinical text, legal documents, and biomedical literature.	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.