Comparer des méthodes

	TF-IDF	Word2Vec
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	1988	2013
Auteur d'origine	Salton & Buckley	Tomas Mikolov et al.
Type	Text vectorization / term-weighting scheme	Neural word-embedding model
Source fondatrice	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Apparentées	3	4
Résumé	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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.