পদ্ধতির তুলনা করুন

	বাক্যার্থিক ভূমিকা চিহ্নিতকরণ (SRL)	প্রশ্নোত্তর (QA)
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2002	—
প্রবর্তক	Daniel Gildea & Daniel Jurafsky	—
ধরন	NLP shallow semantic parsing task	NLP text-comprehension task
মৌলিক উৎস	Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.
অপর নাম	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
সম্পর্কিত	3	4
সারসংক্ষেপ	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.	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.