مقایسهٔ روش‌ها

	شناسایی زبان (LID)	بخش‌بندی متن	TF-IDF
حوزه	متن‌کاوی	متن‌کاوی	متن‌کاوی
خانواده	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	—	1997	1988
پدیدآور	—	Marti A. Hearst (TextTiling)	Salton & Buckley
نوع	NLP text-classification task	NLP document-structure / topic-boundary detection	Text vectorization / term-weighting scheme
منبع بنیادین	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
نام‌های دیگر	language detection, LID, Dil Tanımlama (Language Identification)	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
مرتبط	4	4	3
خلاصه	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.	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.	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.