방법 비교

	FastText	나이브 베이즈
분야	딥러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2016	1997
창시자	Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)	Mitchell, T. M. (textbook treatment)
유형	Subword embedding model and linear text classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
원전	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.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
별칭	fastText, fast text, subword embedding, character n-gram embedding	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
관련	2	4
요약	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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.