Machine learning

FastText

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.

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Sources

  1. 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. DOI: 10.18653/v1/e17-2068
  2. Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146. DOI: 10.1162/tacl_a_00051
  3. Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers. ISBN: 978-1-62705-298-6

Related methods

Referenced by

ScholarGateFastText (FastText: Subword-Level Word Embeddings and Efficient Text Classification). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/fasttext