Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence
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CitationKeller, Mikaela, Clark C. Freifeld, and John S. Brownstein. 2009. Automated vocabulary discovery for geo-parsing online epidemic intelligence. BMC Bioinformatics 10: 385.
AbstractBackground 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:8148864
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