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This paper introduces the first version of the NUB ES corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty. The article includes an exhaustive comparison with similar corpora in Spanish, and presents the main annotation and design decisions. Additionally, we perform preliminary experiments using deep learning algorithms to validate the annotated dataset.

Este es un resumen de la tesis escrita por Jon Alkorta bajo la supervisi ́ondel Dr. Koldo Gojenola (Departamento de Lenguajes y Sistemas Inform ́aticos) yel Dr. Mikel Iruskieta (Departamento de Did ́actica de la Lengua y la Literatura)y presentada en la Universidad del Pa ́ıs Vasco (UPV/EHU).

Zero-shot learning is a very promising research topic. For a vision-based action recognition system, for instance, zero-shot learning allows to recognise actions never seen during the training phase. Previous works in zero-shot action recognition have exploited in several ways the visual appearance of input videos to infer actions. Here, we propose to add external knowledge to improve the performance of purely vision-based systems. Specifically, we have explored three different sources of knowledge in the form of text corpora.