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Biomedical concept normalization links concept mentions in texts to asemantically equivalent concept in a biomedical knowledge base. This task ischallenging as concepts can have different expressions in natural languages,e.g. paraphrases, which are not necessarily all present in the knowledge base.Concept normalization of non-English biomedical text is even more challengingas non-English resources tend to be much smaller and contain less synonyms. Toovercome the limitations of non-English terminologies we propose across-lingual candidate search for concept normalization using acharacter-based neural translation model trained on a multilingual biomedicalterminology. Our model is trained with Spanish, French, Dutch and Germanversions of UMLS. The evaluation of our model is carried out on the FrenchQuaero corpus, showing that it outperforms most teams of CLEF eHealth 2015 and2016. Additionally, we compare performance to commercial translators onSpanish, French, Dutch and German versions of Mantra. Our model performssimilarly well, but is free of charge and can be run locally. This isparticularly important for clinical NLP applications as medical documentsunderlay strict privacy restrictions.

Cross-lingual Candidate Search for Biomedical Concept Normalization