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dc.contributor.authorKeller, Mikaela
dc.contributor.authorFreifeld, Clark C
dc.contributor.authorBrownstein, John Samuel
dc.date.accessioned2012-02-09T19:22:18Z
dc.date.issued2009
dc.identifier.citationKeller, Mikaela, Clark C. Freifeld, and John S. Brownstein. 2009. Automated vocabulary discovery for geo-parsing online epidemic intelligence. BMC Bioinformatics 10: 385.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:8148864
dc.description.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.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi: 10.1186/1471-2105-10-385en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2787530/pdf/en_US
dash.licenseLAA
dc.titleAutomated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligenceen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Bioinformaticsen_US
dash.depositing.authorKeller, Mikaela
dc.date.available2012-02-09T19:22:18Z
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dc.identifier.doi10.1186/1471-2105-10-385*
dash.contributor.affiliatedKeller, Mikaela
dash.contributor.affiliatedBrownstein, John


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