Comparer des méthodes

	Propagation d'étiquettes	Word2Vec
Domaine	Apprentissage automatique	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2002	2013
Auteur d'origine	Zhu, X. & Ghahramani, Z.	Tomas Mikolov et al.
Type	Graph-based semi-supervised classification	Neural word-embedding model
Source fondatrice	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Apparentées	3	4
Résumé	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.	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.