Name
Abstract | ||
|---|---|---|
We describe the EMBERS AutoGSR system that conducts automated coding of civil unrest events from news articles published in multiple languages. The nuts and bolts of the AutoGSR system constitute an ecosystem of filtering, ranking, and recommendation models to determine if an article reports a civil unrest event and, if so, proceed to identify and encode specific characteristics of the civil unrest event such as the when, where, who, and why of the protest. AutoGSR is a deployed system for the past 6 months continually processing data 24x7 in languages such as Spanish, Portuguese, English and encoding civil unrest events in 10 countries of Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. We demonstrate the superiority of AutoGSR over both manual approaches and other state-of-the-art encoding systems for civil unrest. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2939672.2939737 | KDD |
Keywords | Field | DocType |
event extraction,event encoding,text mining | Nuts and bolts,Latin Americans,Ranking,Computer science,Portuguese,Coding (social sciences),Economy,Artificial intelligence,Unrest,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.55 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parang Saraf | 1 | 155 | 11.98 |
| Naren Ramakrishnan | 2 | 1913 | 176.25 |