Automated vocabulary discovery for geo-parsing online epidemic intelligence

• Main Authors: Freifeld Clark C, Keller Mikaela, Brownstein John S
• Format: Article
• Language: English
• Published: BMC 2009-11-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/10/385

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.</p>