Comparer des méthodes

	Évaluation de l'acceptabilité linguistique	Embeddings BERT	TF-IDF
Domaine	Fouille de textes	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1957 (theory); 2019 (neural benchmark — CoLA)	2019	1988
Auteur d'origine	Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019)	Devlin, Chang, Lee & Toutanova (Google AI)	Salton & Buckley
Type	NLP binary/continuous classification task	Contextual transformer text-representation method	Text vectorization / term-weighting scheme
Source fondatrice	Warstadt, A., Singh, A. & Bowman, S. (2019). Neural Network Acceptability Judgments. Transactions of the Association for Computational Linguistics, 7, 625–641.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Alias	grammaticality judgment, acceptability judgment, CoLA task, Dilbilgisel Kabul Edilebilirlik Değerlendirme	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Apparentées	4	4	3
Résumé	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.