Journal

ABSTRACT Objective: To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish.

El objetivo del proyecto es desarrollar, probar y difundir nuevos recursos, nuevas herramientas y aplicaciones lingüı́sticas innovadoras para mejorar el nivel de digitalización del aragonés, vasco y occitano.

Social networks like Twitter are increasingly important in the creation of new ways of communication. They have also become useful tools for social and linguistic research due to the massive amounts of public textual data available. This is particularly important for less resourced languages, as it allows to apply current natural language processing techniques to large amounts of unstructured data. In this work, we study the linguistic and social aspects of young and adult people’s behaviour based on their tweets’ contents and the social relations that arise from them.

This paper presents a successful domain adaptation of a general neural machine translation (NMT) system using a bilingual corpus created with captions for images in Wiki- media Commons for the Spanish-Basque and English-Irish pairs. Keywords: Machine Translation, Low-resource languages, Bilingual corpora, Language resources from Wikipedia

Background Automatic extraction of morbid disease or conditions contained in Death Certificates is a critical process, useful for billing, epidemiological studies and comparison across countries. The fact that these clinical documents are written in regular natural language makes the automatic coding process difficult because, often, spontaneous terms diverge strongly from standard reference terminology such as the International Classification of Diseases (ICD). Objective