Automated Spelling Correction for Clinical Text Mining in Russian
Ksenia Balabaeva, Anastasia A. Funkner, Sergey Valerevich Kovalchuk · arXiv (Cornell University) · 2020
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.