Automated vocabulary discovery for geo-parsing online epidemic intelligence
Author(s)
Keller, Mikaela; Freifeld, Clark C.; Brownstein, John S.
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Background
Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human.
Results
Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon.
Conclusion
The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.
Date issued
2009-11Department
Harvard University--MIT Division of Health Sciences and Technology; Program in Media Arts and Sciences (Massachusetts Institute of Technology)Journal
BMC Bioinformatics
Publisher
BioMed Central Ltd.
Citation
Keller, Mikaela, Clark Freifeld, and John Brownstein. “Automated vocabulary discovery for geo-parsing online epidemic intelligence.” BMC Bioinformatics 10.1 (2009): 385.
Version: Final published version
ISSN
1471-2105