The Coronavirus pandemic has heightened the demand for technological solutions capable of gathering and monitoring data automatically, quickly, and securely. To achieve this need, the Plantão Coronavirus chatbot has been made available to the population of Ceará State in Brazil. This chatbot employs automated symptom detection technology through Natural Language Processing (NLP). The proposal of this work is a symptom tracker, which is a neural network that processes texts and captures symptoms in messages exchanged between citizens of the state and the Plantão Coronavirus nurse/doctor, i.e., clinical conversations. The model has the ability to recognize new patterns and has identified a high incidence of altered psychological behaviors, including anguish, anxiety, and sadness, among users who tested positive or negative for Covid-19. As a result, the tool has emphasized the importance of expanding coverage through community mental health services in the state.

Unstructured text documents such as news and blogs often present references to places. Those references, called toponyms, can be used in various applications like disaster warning and touristic planning. However, obtaining the correct coordinates for toponyms, called geocoding, is not easy since it’s common for places to have the same name as other locations. The process becomes even more challenging when toponyms appear in adjectival form, as they are different from the place’s actual name. This paper addresses the geocoding task and aims to improve, through a heuristic approach, the process for adjectival toponyms. So first, a baseline geocoder is defined through experimenting with a set of heuristics. After that, the baseline is enhanced by adding a normalization step to map adjectival toponyms to their noun form at the beginning of the geocoding process. The results show improved performance for the enhanced geocoder compared to the baseline and other geocoders.