Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence
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Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence
| Title: | Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence |
| Author: |
Freifeld, Clark C; Keller, Mikaela; Brownstein, John Samuel
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Keller, Mikaela, Clark C. Freifeld, and John S. Brownstein. 2009. Automated vocabulary discovery for geo-parsing online epidemic intelligence. BMC Bioinformatics 10: 385. |
| Full Text & Related Files: |
2787530.pdf (469.5Kb; PDF) 
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| 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. |
| Published Version: | doi: 10.1186/1471-2105-10-385 |
| Other Sources: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2787530/pdf/ |
| Terms of Use: | This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:8148864 |
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