Сравнение на методи

	Оценка на лингвистична приемливост	Класификация на текст
Област	Извличане на текст	Извличане на текст
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1957 (theory); 2019 (neural benchmark — CoLA)	—
Създател	Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019)	—
Тип	NLP binary/continuous classification task	Supervised NLP classification task
Основополагащ източник	Warstadt, A., Singh, A. & Bowman, S. (2019). Neural Network Acceptability Judgments. Transactions of the Association for Computational Linguistics, 7, 625–641.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Други названия	grammaticality judgment, acceptability judgment, CoLA task, Dilbilgisel Kabul Edilebilirlik Değerlendirme	text categorization, document classification, topic classification, metin sınıflandırma
Свързани	4	4
Резюме	Linguistic acceptability assessment is a natural-language-processing task that automatically estimates whether a sentence would be judged grammatically acceptable by a native speaker of the target language. Grounded in Chomsky's (1957) distinction between grammatical and ungrammatical utterances, the task was formalised as a neural benchmark by Warstadt, Singh and Bowman (2019) through the Corpus of Linguistic Acceptability (CoLA). It is used in language-learning research, linguistics studies, and quality auditing of natural-language-generation (NLG) systems.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.