Methoden vergleichen

	Semantische Rollenmarkierung (SRL)	Benannte Entitätenerkennung (NER)
Fachgebiet	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2002	—
Urheber	Daniel Gildea & Daniel Jurafsky	—
Typ	NLP shallow semantic parsing task	NLP sequence-labelling task
Wegweisende Quelle	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.
Aliasnamen	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Verwandt	3	3
Zusammenfassung	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.