방법 비교

	텍스트 채우기	BERT 임베딩
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	1953 (cloze); 2019 (neural span infilling)	2019
창시자	Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)	Devlin, Chang, Lee & Toutanova (Google AI)
유형	NLP conditional text generation task	Contextual transformer text-representation method
원전	Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
별칭	cloze procedure, cloze test, masked language modeling, span infilling	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
관련	4	4
요약	Text infilling is a natural-language-processing task that completes missing words, phrases, or spans in a document by exploiting the surrounding context. Introduced as the cloze procedure by Wilson L. Taylor in 1953 as a readability measure, it was reformulated for neural models by Zhu et al. (2019) and is now used for data augmentation, writing assistance, and language-model evaluation.	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.