השוואת שיטות

	נורמליזציית טקסט	תיוג חלקי דיבור (POS Tagging)
תחום	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline
שנת המקור	—	—
הוגה השיטה	—	—
סוג	NLP preprocessing pipeline	NLP sequence-labelling task
מקור מכונן	Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015.	Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP.
כינויים	Metin Normalleştirme, noisy-text normalization, text standardisation, lexical normalisation	part-of-speech tagging, grammatical tagging, Sözcük Türü Etiketleme (POS Tagging)
קשורות	3	3
תקציר	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).	Part-of-speech tagging assigns a grammatical category label — noun, verb, adjective, and so on — to every word in a text. It is a foundational natural-language-processing task, formalised as a statistical model by Ratnaparkhi (1996) and packaged into widely used toolkits such as Stanford CoreNLP (Manning et al., 2014), and it serves as a preliminary step for syntactic analysis and information extraction.