2020/01/01 by Ksenia Balabaeva, Anastasia A. Funkner, Anastasia Funkner +2 · 7 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Biomedical Text Mining and Ontologies #Computer science #Linguistics #Natural Language Processing Techniques #Natural language processing #Philosophy #Spelling #Topic Modeling #cs.CL
paper · pdf · open access · doi:10.3233/shti200119
published in Studies in health technology and informatics 270, 43-47 (IOS Press) · This paper is accepted for publication to MIE 2020 Conference
openalex publication_date 2020/01/01 · arxiv created 2020/04/10 · arxiv updated 2020/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.