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Automated Spelling Correction for Clinical Text Mining in Russian
Ksenia Balabaeva, Anastasia Funkner, Sergey Kovalchuk
43 - 47
10.3233/SHTI200119
Research Article
Studies in Health Technology and Informatics
Volume 270: Digital Personalized Health and Medicine
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.

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