Porovnať metódy

	Sémantické označovanie rolí (SRL)	Rozpoznávanie pomenovaných entít (NER)	Zodpovedanie otázok (QA)
Odbor	Dolovanie textu	Dolovanie textu	Dolovanie textu
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	2002	—	—
Tvorca	Daniel Gildea & Daniel Jurafsky	—	—
Typ	NLP shallow semantic parsing task	NLP sequence-labelling task	NLP text-comprehension task
Pôvodný zdroj	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.	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.
Ďalšie názvy	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
Príbuzné	3	3	4
Zhrnutie	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.	Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.