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Leveraging SNOMED CT terms and relations for machine translation of clinical texts from Basque to Spanish

We present a method for machine translation of clinical texts without using bilingual clinical texts, leveraging the rich terminology and structure of the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), which is considered the most comprehensive, multilingual clinical health care terminology collection in the world. We evaluate our method for Basque to Spanish translation, comparing the performance with and without using clinical domain resources.

Multilingual segmentation based on neural networks and pre-trained word embeddings

The DISPRT 2019 workshop has organized a shared task aiming to identify cross-formalism and multilingual discourse segments.
Elementary Discourse Units (EDUs) are quite similar across different theories. Segmentation is the very first stage on the way of rhetorical annotation. Still, each annotation project adopted several decisions with consequences not only on the annotation of the relational discourse structure but also at the segmentation stage.
In this shared task, we have employed pre-trained word embeddings, neural networks (BiLSTM+CRF) to perform the segmentation.

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