Comparer des méthodes

	Étiquetage sémantique de rôles (SRL)	Reconnaissance d'entités nommées (REN)
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	2002	—
Auteur d'origine	Daniel Gildea & Daniel Jurafsky	—
Type	NLP shallow semantic parsing task	NLP sequence-labelling task
Source fondatrice	Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Alias	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Apparentées	3	3
Résumé	Semantic role labeling, introduced by Gildea and Jurafsky in 2002, is a natural-language-processing task that assigns semantic roles — who did what to whom, where, when, and how — to the components around a verb (predicate) in a sentence. It turns plain text into structured predicate-argument representations and is a foundational tool for event extraction.	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.