A Machine Translation Approach for Medical Terms

Renato, Alejandro; Castaño, José M.; Williams, Maria del Pilar Avila; Berinsky, Hernán; Gambarte, Maria Laura; Park, Hee Joon; Pérez-Rey, David; Otero, Carlos; Luna, Daniel

doi:10.5220/0006555003690378

Cited by 8 publications

(9 citation statements)

References 4 publications

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“…For both experiments they used for translation Bing, Google Translate and their system M-SMT. In the first experiment their system achieved the highest score which is (58.9) [20] using BLEU score. In the second experiment their system achieved (86.7) [20].…”

Section: Related Workmentioning

confidence: 99%

“…In the first experiment their system achieved the highest score which is (58.9) [20] using BLEU score. In the second experiment their system achieved (86.7) [20]. That shows that their system achieved the highest score.…”

Section: Related Workmentioning

confidence: 99%

“…Renato, et al [20] discussed translating clinical term descriptions from Spanish to Brazilian Portuguese. HIBA dictionary was used as a Spanish dictionary.…”

Section: Related Workmentioning

confidence: 99%

“…As showed that the second experiment achieved higher scores in all translation systems. However although they achieved high scores there were some errors as [20]: OOV words are usually translated into English or left in Spanish, a part of the corpus had words with spelling in European Portuguese, Compound medical terms, especially drugs with a hyphen, possibly misaligned in training.…”

Section: Related Workmentioning

confidence: 99%

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English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

Ehab¹,

Amer²,

Gadallah³

2019

ijacsa

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The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language. A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper. Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic. To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory. The system constructed achieved 77.17 score for the first dataset and 63.85 score for the second which were the highest score using BLEU score.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Renato, et al [20] discussed translating clinical term descriptions from Spanish to Brazilian Portuguese. HIBA dictionary was used as a Spanish dictionary.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

Ehab¹,

Amer²,

Gadallah³

2019

ijacsa

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show abstract

“…The size of SNOMED CT renders manual translation extremely onerous. In the light of the popularisation of free, web-based machine translation (MT) tools [2][3][4][5][6], we are developing an approach called MTP aiming at assisting human translation in SNOMED CT localisation projects. For a chosen target language, MTP generates a scored output of translation candidates (TCs) for each input FSN.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of German Translation Candidates for SNOMED CT Textual Descriptions

Prunotto

Schulz

Boeker

2021

Studies in Health Technology and Informatics

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We present an approach called MTP (multiple translation paths) aiming at assisting human translation in SNOMED CT localisation projects based on free, web-based machine translation tools. For a chosen target language, MTP generates a scored output of translation candidates (TCs) for each input concept. This paper describes the basic idea of MTP, the distribution of its output TCs and discusses typical examples with German as target language. The MTP approach capitalises on combinatorial growth by the combination of input languages, support languages, and translation engines. We applied MTP on the SNOMED CT Starter Set, using Google Translator, DeepL and Systran, together with the four source languages English, Spanish, Swedish and French, and Danish, Dutch, Norwegian, Italian, Portuguese, Polish and Russian as support languages. The descriptive assessment of TC variety, together with an analysis of typical results is the focus of this paper. MTP defines, for each input concept, TPs by the combination of input languages, support languages and translation engines, resulting in 91 translation results with various degrees of co-incidence (cardinality). The most configurations produce an average number of TCs indicating that the same TC is often derived via different translation paths. Combinations of translation engines result in distributions with a higher number of distinct TCs per concept. We present work in progress on using machine translation (MT) for terminology translation, by leveraging several free MT tools fed by different languages and language combinations. A first qualitative analysis was promising and supports our hypothesis that a majority voting applied to many translation candidates yields higher quality results than from one single engine and input language.

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Machine translation of standardised medical terminology using natural language processing: A scoping review

Noll,

Frischen,

Boeker

et al. 2023

New Biotechnology

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A Machine Translation Approach for Medical Terms

Cited by 8 publications

References 4 publications

English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

Automatic Generation of German Translation Candidates for SNOMED CT Textual Descriptions

Machine translation of standardised medical terminology using natural language processing: A scoping review

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