Salīdzināt metodes

	FastText	GloVe iegulšanas
Nozare	Dziļā mācīšanās	Teksta ieguve
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2016	2014
Autors	Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)	Pennington, Socher & Manning
Tips	Subword embedding model and linear text classifier	Static word-embedding model
Pirmavots	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.	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.
Citi nosaukumi	fastText, fast text, subword embedding, character n-gram embedding	GloVe, global vectors, GloVe Kelime Gömülmeleri
Saistītās	2	3
Kopsavilkums	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.	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.