방법 비교

	Self-supervised Word2Vec	순환 신경망
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2013	1986–1990
창시자	Mikolov, T., Chen, K., Corrado, G., & Dean, J.	Rumelhart, D. E.; Elman, J. L.
유형	Self-supervised neural word embedding	Sequential neural network
원전	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.
별칭	Word2Vec, word embeddings, Skip-gram model, CBOW model	RNN, Elman network, Jordan network, simple recurrent network
관련	3	3
요약	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.