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- W2945808722 abstract "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." @default.
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- W2945808722 date "2019-01-01" @default.
- W2945808722 modified "2023-10-18" @default.
- W2945808722 title "Misspelling Oblivious Word Embeddings" @default.
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- W2945808722 doi "https://doi.org/10.18653/v1/n19-1326" @default.
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