Jämför metoder
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| FastText× | GloVe-inbäddningar× | |
|---|---|---|
| Ämnesområde≠ | Djupinlärning | Textutvinning |
| Familj≠ | Machine learning | Process / pipeline |
| Ursprungsår≠ | 2016 | 2014 |
| Upphovsperson≠ | Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research) | Pennington, Socher & Manning |
| Typ≠ | Subword embedding model and linear text classifier | Static word-embedding model |
| Ursprungskälla≠ | 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 ↗ | Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP. DOI ↗ |
| Alias≠ | fastText, fast text, subword embedding, character n-gram embedding | GloVe, global vectors, GloVe Kelime Gömülmeleri |
| Närliggande≠ | 2 | 3 |
| Sammanfattning≠ | 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. |
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