Reducing the Need for Double Annotation
Dmitriy Dligach, Martha Stone Palmer · 2012
The quality of annotated data is crucial for supervised learning. To eliminate errors in single annotated data, a second round of annotation is often used. However, is it absolutely necessary to double annotate every example? We show that it is possible to reduce the amount of the second round of annotation by more than half without sacrificing the performance. 1