Usporedite metode

	Semantičko označavanje uloga (SRL)	Otkrivanje događaja	Prepoznavanje imenovanih entiteta (NER)
Područje	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Obitelj	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	2002	—	—
Tvorac	Daniel Gildea & Daniel Jurafsky	—	—
Vrsta	NLP shallow semantic parsing task	NLP information-extraction task	NLP sequence-labelling task
Temeljni izvor	Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.	Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Drugi nazivi	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	event extraction, Olay Tespiti (Event Detection)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Srodne	3	4	3
Sažetak	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.	Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and historical research.	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.