  This paper introduces the first version of the NUBes 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.
As far as we know, NUBes is the largest publicly available corpus for negation
in Spanish and the first that also incorporates the annotation of speculation
cues, scopes, and events.
