Porovnať metódy

	Rozpoznávanie pomenovaných entít (NER)	Zodpovedanie otázok (QA)
Odbor	Dolovanie textu	Dolovanie textu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	—	—
Tvorca	—	—
Typ	NLP sequence-labelling task	NLP text-comprehension task
Pôvodný zdroj	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	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
Príbuzné	3	4
Zhrnutie	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.