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- W2972498489 abstract "In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature. The extraction of this knowledge is complicated by colloquial language use and misspellings. Yet, lexical normalization of such data has not been addressed properly. This paper presents an unsupervised, data-driven spelling correction module for medical social media. Our method outperforms state-of-the-art spelling correction and can detect mistakes with an F0.5 of 0.888. Additionally, we present a novel corpus for spelling mistake detection and correction on a medical patient forum." @default.
- W2972498489 created "2019-09-19" @default.
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- W2972498489 date "2019-01-01" @default.
- W2972498489 modified "2023-09-25" @default.
- W2972498489 title "Lexical Normalization of User-Generated Medical Text" @default.
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- W2972498489 doi "https://doi.org/10.18653/v1/w19-3202" @default.
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