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arXiv · 2004.04987

Automated Spelling Correction for Clinical Text Mining in Russian

Ksenia Balabaeva·Anastasia Funkner·Sergey Kovalchuk
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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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Ksenia Balabaeva, Anastasia Funkner, Sergey Kovalchuk. 2020-04-10. Automated Spelling Correction for Clinical Text Mining in Russian. https://doi.org/10.3233/shti200119

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