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Enriching Word Vectors with Subword Information
Piotr Bojanowski, Edouard Grave, Armand Joulin et al., 2017. Transactions of the Association for Computational Linguistics.
The fastText approach to word vectors: within a skip-gram model, a word's vector is the sum of vectors for the character n-grams it contains, so vectors can also be built for words never seen in training.
What it's used for here
The inductive-methods survey proposes its subword vectors as a spelling-only comparison when building embeddings from the small Voynich corpus.
Cited by on this site 1 record
1 methods surveys
SRCInductive methods
Cite it
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics. https://doi.org/10.1162/tacl_a_00051
@article{bojanowski2017enriching,
author = {Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
title = {Enriching Word Vectors with Subword Information},
journal = {Transactions of the Association for Computational Linguistics},
year = {2017},
doi = {10.1162/tacl_a_00051},
url = {https://doi.org/10.1162/tacl_a_00051}
}TY - JOUR TI - Enriching Word Vectors with Subword Information AU - Bojanowski, Piotr AU - Grave, Edouard AU - Joulin, Armand AU - Mikolov, Tomas PY - 2017 JO - Transactions of the Association for Computational Linguistics DO - 10.1162/tacl_a_00051 UR - https://doi.org/10.1162/tacl_a_00051 ER -
Bojanowski, Piotr et al.. “Enriching Word Vectors with Subword Information.” Transactions of the Association for Computational Linguistics (2017). https://doi.org/10.1162/tacl_a_00051.