Lexical Normalization of User-Generated Medical Forum Data

Author(s):
Dirkson, A.R. (0000-0002-4332-0296); Verberne, S. (0000-0002-9609-9505); Kraaij, W. (0000-0001-7797-619X)
Editor(s):
Weissenbacher D., Gonzalez-Hernandez G.
Published
2019
Publication type
Article in monograph or in proceedings

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.

Date issued
2019-08-01
Language
English
Publisher
Association for Computational Linguistics
Is part of
Proceedings of the Fourth Social Media Mining for Health Applications (SMM4H) Workshop & Shared Task (2019), p. 11-20
hdl
1887/80813
doi
10.18653/v1/W19-3202
URL
https://www.aclweb.org/anthology/W19-3202

Conference

Meeting name
Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Meeting location
Florence, Italy
Meeting date
2019-08-02 - 2019-08-02