So sánh phương pháp

	Word2Vec tự giám sát	FastText
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2013	2016
Người khởi xướng	Mikolov, T., Chen, K., Corrado, G., & Dean, J.	Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)
Loại	Self-supervised neural word embedding	Subword embedding model and linear text classifier
Công trình gốc	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).	Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. In Proceedings of EACL 2017, Short Papers, pp. 427–431. ACL.
Tên gọi khác	Word2Vec, word embeddings, Skip-gram model, CBOW model	fastText, fast text, subword embedding, character n-gram embedding
Liên quan	3	2
Tóm tắt	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.	FastText is a word embedding and text classification framework developed by Facebook AI Research (Joulin, Bojanowski, Grave, and Mikolov, 2016–2017) that represents each word as the sum of its character n-gram vectors, allowing it to construct meaningful representations for unseen and morphologically rich words and to perform near state-of-the-art text classification orders of magnitude faster than deep neural network alternatives.