Sammenlign metoder

	Vurdering af lingvistisk acceptabilitet	TF-IDF
Fagområde	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1957 (theory); 2019 (neural benchmark — CoLA)	1988
Ophavsperson	Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019)	Salton & Buckley
Type	NLP binary/continuous classification task	Text vectorization / term-weighting scheme
Oprindelig kilde	Warstadt, A., Singh, A. & Bowman, S. (2019). Neural Network Acceptability Judgments. Transactions of the Association for Computational Linguistics, 7, 625–641.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Aliasser	grammaticality judgment, acceptability judgment, CoLA task, Dilbilgisel Kabul Edilebilirlik Değerlendirme	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relaterede	4	3
Resumé	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.