A Corpus for Outbreak Detection of Diseases Prevalent in Latin America

JOSE OCHOA-LUNA; ANTONELLA DELLANZO; VIVIANA COTIK

Punta Cana (evento virtual)

Conferencia; Conference on Computational Natural Language Learning (CoNLL); 2020

Special Interest Group on Natural Language Learning of the Association for Computational Linguistics (ACL)

In this paper we present an annotated corpus which can be used for training and testing algorithms to automatically extract information about diseases outbreaks from news and health reports. We also propose initial approaches to extract information from it. The corpus has been constructed with two main tasks in mind. The first one, to extract entities about outbreaks such as disease, host, locationamong others. The second one, to retrieve relations among entities, for instance, in such geographic location fifteen cases of a given disease were reported. Overall, our goal is to offer resources and tools to perform an automated analysis so as to support early detection of disease outbreaks and therefore diminish their spreading.