  Biomedical concept normalization links concept mentions in texts to a
semantically equivalent concept in a biomedical knowledge base. This task is
challenging 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 challenging
as non-English resources tend to be much smaller and contain less synonyms. To
overcome the limitations of non-English terminologies we propose a
cross-lingual candidate search for concept normalization using a
character-based neural translation model trained on a multilingual biomedical
terminology. Our model is trained with Spanish, French, Dutch and German
versions of UMLS. The evaluation of our model is carried out on the French
Quaero corpus, showing that it outperforms most teams of CLEF eHealth 2015 and
2016. Additionally, we compare performance to commercial translators on
Spanish, French, Dutch and German versions of Mantra. Our model performs
similarly well, but is free of charge and can be run locally. This is
particularly important for clinical NLP applications as medical documents
underlay strict privacy restrictions.
