手法を比較

	Text Infilling	テキスト分類
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	Supervised NLP classification task
原典	Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
別名	cloze procedure, cloze test, masked language modeling, span infilling	text categorization, document classification, topic classification, metin sınıflandırma
関連	4	4
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.	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.