手法を比較

	テキスト正規化	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	—	—
提唱者	—	—
種類	NLP preprocessing pipeline	NLP sequence-labelling task
原典	Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
別名	Metin Normalleştirme, noisy-text normalization, text standardisation, lexical normalisation	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
関連	3	3
概要	Text normalization is an NLP preprocessing pipeline that converts noisy, abbreviated, or misspelled text — such as SMS messages, social-media posts, and OCR output — into a clean, standardised form. It is a prerequisite step for virtually every downstream NLP task, ensuring that inconsistent surface forms do not degrade tokenisation, parsing, or classification. The method gained systematic academic treatment through Baldwin and Li (2015) and Sproat and Jaitly (2017).	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.