Comparar métodos

	Segmentación de texto	Identificación de Idiomas (LID)	TF-IDF
Campo	Minería de texto	Minería de texto	Minería de texto
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	1997	—	1988
Autor original	Marti A. Hearst (TextTiling)	—	Salton & Buckley
Tipo	NLP document-structure / topic-boundary detection	NLP text-classification task	Text vectorization / term-weighting scheme
Fuente seminal	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.
Alias	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
Relacionados	4	4	3
Resumen	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.