Lexical Normalization of User-Generated Medical Text

Anne Dirkson, Suzan Verberne, Wessel Kraaij


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.
Anthology ID:
W19-3202
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–20
Language:
URL:
https://www.aclweb.org/anthology/W19-3202
DOI:
10.18653/v1/W19-3202
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3202.pdf