Catatan bukti metode

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.

Sources recorded, not reviewed

Catatan sumber

Kutipan disalin apa adanya dari catatan sumber metode. Tidak ada verifikasi tingkat klaim yang disimpulkan darinya.

FastText: Subword-Level Word Embeddings and Efficient Text Classification

Catatan metode taksonomi · ml-model / deep-learning

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
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
Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers. · ISBN 978-1-62705-298-6

Buka metode lengkap

Klaim yang dikurasi

Klaim tersimpan dalam buku besar bukti, masing-masing dengan penilaiannya sendiri.

Belum ada klaim yang dikurasi

Tampilan ini tidak menciptakan penilaian klaim ketika buku besar tidak memilikinya.

Metode terkait

Dihasilkan dari grafik metode dan ditampilkan sebagai relasi yang disarankan mesin — tidak ada klaim bukti yang disimpulkan.

Same method familyNaive Bayesmachine-suggested · Relational suggestion, not evidence.See alsoWord2Vecmachine-suggested · Relational suggestion, not evidence.