Automated Spelling Correction for Clinical Text Mining in Russian
This work addresses the problem of misspelling in clinical text mining for Russian-language medical applications, though it appears incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of automated spelling correction for clinical text mining in Russian by combining string distance algorithms with machine learning embedding methods, achieving an overall precision of 0.86, lexical precision of 0.975, and error precision of 0.74.
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.