Сравнение на методи

	Commonsense Reasoning	Семантично ролево етикетиране (SRL)
Област	Извличане на текст	Извличане на текст
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2019 (landmark benchmarks)	2002
Създател	Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019)	Daniel Gildea & Daniel Jurafsky
Тип	NLP reasoning task	NLP shallow semantic parsing task
Основополагащ източник	Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI.	Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.
Други названия	commonsense NLP, if-then reasoning, Sağduyu Akıl Yürütme (Commonsense Reasoning)	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)
Свързани	6	3
Резюме	Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap et al., 2019), an if-then commonsense knowledge graph, and HellaSwag (Zellers et al., 2019), a sentence-completion challenge that exposed gaps in machine understanding of everyday events.	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.