Papers › Misspelling Oblivious Word Embeddings

Misspelling Oblivious Word Embeddings

23 May 2019NAACL 2019 6arXiv:1905.09755archive 2025-07-28

Bora Edizel, Aleksandra Piktus, Piotr Bojanowski, Rui Ferreira, Edouard Grave, Fabrizio Silvestri

In this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of out-of-vocabulary words. We propose a method combining FastText with subwords and a supervised task of learning misspelling patterns. In our method, misspellings of each word are embedded close to their correct variants. We train these embeddings on a new dataset we are releasing publicly. Finally, we experimentally show the advantages of this approach on both intrinsic and extrinsic NLP tasks using public test sets.

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bitbucket.org/bedizel/moe officialmentioned in paper report
dleemiller/string-noise mentioned on GitHub report
facebookresearch/moe mentioned on GitHub report

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Word Embeddings

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fastText

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