Name
Title | ||
|---|---|---|
Error-Correction and Aggregation in Crowd-Sourcing of Geopolitical Incident Information. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2015 | SBP | Data mining,Annotation,Computer science,Error detection and correction,Bayesian network,Artificial intelligence,Majority rule,Discriminative model,Machine learning,Scalability |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander G. Ororbia II | 1 | 1 | 2.11 |
| Yang Xu | 2 | 4 | 2.15 |
| Vito D'Orazio | 3 | 0 | 1.01 |
| David Reitter | 4 | 5 | 1.49 |