| Autor: |
MALO, Sadouanouan, TRAORÉ, Yaya, THIOMBIANO, Julie |
| Předmět: |
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| Zdroj: |
CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings; 2021, Issue 16, p1-4, 4p |
| Abstrakt: |
For decades, infectious diseases have been claiming many victims around the world. Even today, despite technology and scientific advances, these diseases are still topical, evolving in the form of outbreaks that wreak havoc and take doctors and public health professionals by surprise. Discovered and treated in time can prevent possible epidemics or pandemics. One way of doing this is to make the many resources that are shared via the internet speak for themselves. This will consist of intelligently processing this massive resource of information, while taking into account the complexity linked to the fact that this information may be in various formats, sometimes described as non-standard, thus limiting the application of NLP techniques. In this document, we present our work which consists in extracting events and qualitative data from the text for the monitoring of infectious diseases. The extraction process will be based on Machine Learning techniques and guided by a domain ontology. Given the number of infectious diseases we will focus our study-on meningitis and COVID. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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