Corpus-based Translation of Ontologies for Improved Multilingual Semantic Annotation

Bretschneider, Claudia; Oberkampf, Heiner; Zillner, Sonja; Bauer, Bernhard; Hammon, Matthias

doi:10.3115/v1/w14-6201

Cited by 3 publications

(3 citation statements)

References 9 publications

(3 reference statements)

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“…Furthermore, as we are interested in entity recognition, a low coverage on the ontology is not correlated with a low coverage for entities in a corpus. In Bretschneider et al (2014), by only translating a small sub-part of an ontology they could improve the coverage of the entities in their corpus by a high margin. It will be interesting to verify this on a dataset on disease recognition.…”

Section: Discussionmentioning

confidence: 99%

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Multilingual enrichment of disease biomedical ontologies

Bouscarrat¹,

Bonnefoy²,

Capponi³

et al. 2020

Preprint

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Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both ontologies, plus Arabic, Chinese and Russian for the second one. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.

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Section: Discussionmentioning

confidence: 99%

“…There has already been some work trying to use opensource knowledge bases to translate biomedical ontologies. Bretschneider et al (2014) obtain a German-English medical dictionary using DBPedia. The goal is to perform information extraction from a German biomedical corpus.…”

Section: Resources and Related Workmentioning

confidence: 99%

Multilingual enrichment of disease biomedical ontologies

Bouscarrat¹,

Bonnefoy²,

Capponi³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…There are mainly two possible solutions to this problem. One is to translate the lexicon itself ( 8 , 9 ) and the other is to translate the reports. Translating the lexicon has the advantage of not requiring continuous translation, i.e.…”

Section: Introductionmentioning

confidence: 99%

Impact of translation on named-entity recognition in radiology texts

Campos

Pedro²,

Couto

2017

Database

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Radiology reports describe the results of radiography procedures and have the potential of being a useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. We created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semi-automatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD we studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms extracted from human translations as our gold standard, we calculated how similar to this standard were the terms extracted using other translations. We found that a completely automatic translation approach using Google leads to F-scores (between 0.861 and 0.868, depending on the extraction approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results we also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. Database URL: https://github.com/lasigeBioTM/MRRAD

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Semantic Splitting of German Medical Compounds

Bretschneider

Zillner

2015

Text, Speech, and Dialogue

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Corpus-based Translation of Ontologies for Improved Multilingual Semantic Annotation

Cited by 3 publications

References 9 publications

Multilingual enrichment of disease biomedical ontologies

Multilingual enrichment of disease biomedical ontologies

Impact of translation on named-entity recognition in radiology texts

Semantic Splitting of German Medical Compounds

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