পদ্ধতির তুলনা করুন

	টেক্সট নর্মালাইজেশন	নামযুক্ত সত্তা শনাক্তকরণ (NER)
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	—	—
প্রবর্তক	—	—
ধরন	NLP preprocessing pipeline	NLP sequence-labelling task
মৌলিক উৎস	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.
অপর নাম	Metin Normalleştirme, noisy-text normalization, text standardisation, lexical normalisation	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
সম্পর্কিত	3	3
সারসংক্ষেপ	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.