dc.contributor.author | Keller, Mikaela | |
dc.contributor.author | Freifeld, Clark C | |
dc.contributor.author | Brownstein, John Samuel | |
dc.date.accessioned | 2012-02-09T19:22:18Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Keller, 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.issn | 1471-2105 | en_US |
dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:8148864 | |
dc.description.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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | BioMed Central | en_US |
dc.relation.isversionof | doi: 10.1186/1471-2105-10-385 | en_US |
dc.relation.hasversion | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2787530/pdf/ | en_US |
dash.license | LAA | |
dc.title | Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence | en_US |
dc.type | Journal Article | en_US |
dc.description.version | Version of Record | en_US |
dc.relation.journal | BMC Bioinformatics | en_US |
dash.depositing.author | Keller, Mikaela | |
dc.date.available | 2012-02-09T19:22:18Z | |
dash.affiliation.other | HMS^Pediatrics-Children's Hospital | en_US |
dash.affiliation.other | HMS^Pediatrics-Children's Hospital | en_US |
dc.identifier.doi | 10.1186/1471-2105-10-385 | * |
dash.contributor.affiliated | Keller, Mikaela | |
dash.contributor.affiliated | Brownstein, John | |