Comparar métodos

	Segmentação de texto	Modelo de Linguagem N-grama	TF-IDF
Área	Mineração de texto	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	1997	—	1988
Autor original	Marti A. Hearst (TextTiling)	—	Salton & Buckley
Tipo	NLP document-structure / topic-boundary detection	Statistical language model	Text vectorization / term-weighting scheme
Fonte seminal	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Outros nomes	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	n-gram model, statistical language model, N-gram Dil Modeli	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relacionados	4	4	3
Resumo	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.	An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.	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.