Automated vocabulary discovery for geo-parsing online epidemic intelligence.
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.


CONCLUSIONS
The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.
Keller
Mikaela
M
Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 300 Longwood Ave, Boston, MA 02115, USA. mikaela.keller@childrens.harvard.edu
Freifeld
Clark C
CC
Brownstein
John S
JS
D000818:Animals; D004196:Disease Outbreaks; D006801:Humans; D020407:Internet; D011159:Population Surveillance / Q000379:methods*; D012984:Software; D014825:Vocabulary