Automated vocabulary discovery for geo-parsing online epidemic intelligence
Name
Keller-2009-Automated vocabulary.pdf
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458.55 KB
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Adobe PDF
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Author(s)
Keller, Mikaela
Freifeld, Clark C.
Brownstein, John S.
Date Issued
November 2009
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
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution
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DOI of Published Version
http://dx.doi.org/10.1186/1471-2105-10-385
