Сравнение методов

	Идентификация языка (LID)	Сегментация текста	TF-IDF
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	—	1997	1988
Автор метода	—	Marti A. Hearst (TextTiling)	Salton & Buckley
Тип	NLP text-classification task	NLP document-structure / topic-boundary detection	Text vectorization / term-weighting scheme
Основополагающий источник	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.
Другие названия	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
Связанные	4	4	3
Сводка	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.