NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts
This addresses the need for annotated data in Spanish clinical NLP, though it is incremental as it builds on existing work in other languages.
The paper introduces NUBES, the first publicly available corpus for negation and uncertainty in Spanish clinical texts, consisting of 29,682 annotated sentences, and validates it with preliminary deep learning experiments.
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.