পদ্ধতির তুলনা করুন

	প্রশ্নোত্তর (QA)	নামযুক্ত সত্তা শনাক্তকরণ (NER)
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	—	—
প্রবর্তক	—	—
ধরন	NLP text-comprehension task	NLP sequence-labelling task
মৌলিক উৎস	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
অপর নাম	QA, machine reading comprehension, Soru Cevaplama (Question Answering)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
সম্পর্কিত	4	3
সারসংক্ষেপ	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.	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.