Σύγκριση μεθόδων

	Τμηματοποίηση Κειμένου	Αναγνώριση Γλώσσας (LID)	TF-IDF
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	1997	—	1988
Δημιουργός	Marti A. Hearst (TextTiling)	—	Salton & Buckley
Τύπος	NLP document-structure / topic-boundary detection	NLP text-classification task	Text vectorization / term-weighting scheme
Θεμελιώδης πηγή	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Εναλλακτικές ονομασίες	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	language detection, LID, Dil Tanımlama (Language Identification)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Συναφείς	4	4	3
Σύνοψη	Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.	Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.