Acquisition of Character Translation Rules for Supporting SNOMED CT Localizations.
Stud Health Technol Inform
; 210: 597-601, 2015.
Article
em En
| MEDLINE
| ID: mdl-25991218
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Semântica
/
Tradução
/
Algoritmos
/
Processamento de Linguagem Natural
/
Reconhecimento Automatizado de Padrão
/
Systematized Nomenclature of Medicine
País/Região como assunto:
Europa
Idioma:
En
Revista:
Stud Health Technol Inform
Assunto da revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Áustria