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2015
Springer

Error-Correction and Aggregation in Crowd-Sourcing of Geopolitical Incident Information

4 years 2 months ago
Error-Correction and Aggregation in Crowd-Sourcing of Geopolitical Incident Information
A discriminative model is presented for crowd-sourcing the annotation of news stories to produce a structured dataset about incidents involving militarized disputes between nation-states. We used a question tree to gather partially redundant data from each crowd worker. A lattice of Bayesian Networks was then applied to error correct the individual worker annotations, the results of which were then aggregated via majority voting. The resulting hybrid model outperformed comparable, state-of-the-art aggregation models in both accuracy and computational scalability.
Alexander G. Ororbia II, Yang Xu, Vito D'Orazio, D
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SBP
Authors Alexander G. Ororbia II, Yang Xu, Vito D'Orazio, David Reitter
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