Foot-and-mouth disease (FMD) is one of the most devastating diseases of farm animals. There is a critical need for countries to have a global FMD situational awareness. Monitoring the online news sources for FMD-related news is an important component of situational awareness. The FMD Lab at UC Davis (http://fmd.ucdavis.edu/) has developed models and systems for global FMD surveillance, including the FMD BioPortal web-based system jointly with the AI Lab at the University of Arizona. They have also been gathering and processing FMD-related news from the FMD World Reference Laboratory, the OIE, the FAO, among others. However, manual searches are necessary identify and integrate the FMD news into their models and systems. This manual work is not only time consuming and labor intensive but may also lead to the loss of some important information.
OBJECTIVE
This paper describes an FMD news monitoring and classification system which can automatically monitor FMD related news from online news data sources, generate news summarization and classify the news into three categories defined by domain experts. The report research is a collaborative effort between the Artificiall Intelligence Lab at the University of Arizona and the FMD Lab at UC Davis.