  There is a broad range of BioNLP tasks for which active learning (AL) can
significantly reduce annotation costs and a specific AL algorithm we have
developed is particularly effective in reducing annotation costs for these
tasks. We have previously developed an AL algorithm called ClosestInitPA that
works best with tasks that have the following characteristics: redundancy in
training material, burdensome annotation costs, Support Vector Machines (SVMs)
work well for the task, and imbalanced datasets (i.e. when set up as a binary
classification problem, one class is substantially rarer than the other). Many
BioNLP tasks have these characteristics and thus our AL algorithm is a natural
approach to apply to BioNLP tasks.
