title: Acquisition of Character Translation Rules for Supporting SNOMED CT Localizations.
abstract: Translating huge medical terminologies like SNOMED CT is costly and time consuming. We present a methodology that acquires substring substitution rules for single words, based on the known similarity between medical words and their translations, due to their common Latin / Greek origin. Character translation rules are automatically acquired from pairs of English words and their automated translations to German. Using a training set with single words extracted from SNOMED CT as input we obtained a list of 268 translation rules. The evaluation of these rules improved the translation of 60% of words compared to Google Translate and 55% of translated words that exactly match the right translations. On a subset of words where machine translation had failed, our method improves translation in 56% of cases, with 27% exactly matching the gold standard.
authors.affiliation: Institute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.
authors.forename: Jose Antonio
authors.initials: JA
authors.lastname: Miñarro-Giménez
authors.affiliation: Jena University Language & Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Hellrich
authors.affiliation: Institute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.
authors.forename: Stefan
authors.initials: S
authors.lastname: Schulz
mesh_terms: D000465:Algorithms; D005060:Europe; D005568:Forms and Records Control / Q000379:methods; D005858:Germany; D000069550:Machine Learning; D008498:Medical Record Linkage / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D039061:Systematized Nomenclature of Medicine; D009626:Terminology as Topic; D014175:Translating