手法を比較

	Text Infilling	固有表現抽出（NER）
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	1953 (cloze); 2019 (neural span infilling)	—
提唱者	Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)	—
種類	NLP conditional text generation task	NLP sequence-labelling task
原典	Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
別名	cloze procedure, cloze test, masked language modeling, span infilling	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
関連	4	3
概要	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.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.