Comparer des méthodes

	Auto-apprentissage avec Word2Vec	GloVe
Domaine	Apprentissage profond	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2013	2014
Auteur d'origine	Mikolov, T., Chen, K., Corrado, G., & Dean, J.	Pennington, Socher & Manning
Type	Self-supervised neural word embedding	Static word-embedding model
Source fondatrice	Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR 2013).	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.
Alias	Word2Vec, word embeddings, Skip-gram model, CBOW model	GloVe, global vectors, GloVe Kelime Gömülmeleri
Apparentées	3	3
Résumé	Word2Vec is a shallow neural network model introduced by Mikolov et al. (2013) that learns dense vector representations of words from large unlabeled text corpora using self-supervised objectives. By training a model to predict surrounding context words (Skip-gram) or a target word from its context (CBOW), it captures rich semantic and syntactic regularities in continuous vector space without any manual annotation.	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.