विधियों की तुलना करें

	सिमेंटिक रोल लेबलिंग (SRL)	प्रश्न उत्तरण (QA)
क्षेत्र	पाठ खनन	पाठ खनन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2002	—
प्रवर्तक	Daniel Gildea & Daniel Jurafsky	—
प्रकार	NLP shallow semantic parsing task	NLP text-comprehension task
मौलिक स्रोत	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.
उपनाम	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
संबंधित	3	4
सारांश	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.