Automated Spelling Correction for Clinical Text Mining in Russian
Computation and Language
2020-07-30 v1
Abstract
The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our overall precision is 0.86, lexical precision - 0.975 and error precision is 0.74. We develop spell checker as a part of medical text mining tool regarding the problems of misspelling, negation, experiencer and temporality detection.
Cite
@article{arxiv.2004.04987,
title = {Automated Spelling Correction for Clinical Text Mining in Russian},
author = {Ksenia Balabaeva and Anastasia Funkner and Sergey Kovalchuk},
journal= {arXiv preprint arXiv:2004.04987},
year = {2020}
}
Comments
This paper is accepted for publication to MIE 2020 Conference