Machine-learned models depend on having human-annotated training data, a tremendously time-consuming and expensive undertaking. Just annotating 500,000 Korean tokens for named entity recognition  takes about eight person months of annotator time (split among four annotators and a project manager). Basis Technology is developing an active learning annotator that will significantly reduce the labor of this critical process, by both increasing the speed of annotation and reducing the number of tagged documents needed to reach required accuracy.