OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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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.