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NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts

2020-04-02LREC 2020Code Available0· sign in to hype

Salvador Lima, Naiara Perez, Montse Cuadros, German Rigau

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Abstract

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.

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