This paper proposes a simple and efficient approach for text classification and representation learning. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.
Joulin et al. REF proposes a simple and efficient baseline classifier that performs as well as deep learning classifiers in terms of accuracy and runs faster.
Bag of Tricks for Efficient Text Classification
ArXiv
ArXiv
Computer Science