An Approach to Reducing Annotation Costs for BioNLP

Abstract

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.

2 pages, 1 figure, 5 tables; appeared in Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing at ACL (Association for Computational Linguistics) 2008