Journal

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).

Multiword expressions can have both idiomatic and literal occurrences. For instance pulling strings can be understood either as making use of one’s influence, or literally. Distinguishing these two cases has been addressed in linguistics and psycholinguistics studies, and is also considered one of the major challenges in MWE processing. We suggest that literal occurrences should be considered in both semantic and syntactic terms, which motivates their study in a treebank.

This paper presents a study about methods for normalization of historical texts. The aim of these methods
is learning relations between historical and contemporary word forms. We have compiled training and test
corpora for different languages and scenarios, and we have tried to read the results related to the features
of the corpora and languages. Our proposed method, based on weighted finite-state transducers, is com-
pared to previously published ones. Our method learns to map phonological changes using a noisy channel