Comparer des méthodes

	Auto-apprentissage avec Word2Vec	Réseau de neurones récurrent
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2013	1986–1990
Auteur d'origine	Mikolov, T., Chen, K., Corrado, G., & Dean, J.	Rumelhart, D. E.; Elman, J. L.
Type	Self-supervised neural word embedding	Sequential neural network
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).	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Alias	Word2Vec, word embeddings, Skip-gram model, CBOW model	RNN, Elman network, Jordan network, simple recurrent network
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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.