Active Learning with Support Vector Machines

This thesis examines the use of support vector machines for active learning using linear, polynomial and radial basis function kernels. In our experiments we used named entity recognition which was treated as a binary task and as a multiclass task and we also tackled shallow parsing. We report savings in annotation costs ranging from 80% to 95% depending on the task. We observed that the distribution of labels in the selected instances during active learning could provide us with a stopping criterion in cases where one class can be considered to be the majority class of the dataset. Finally, using the confidence estimation of the SVM classifier, we define a stopping criterion that appears to be efficient in all our active learning experiments.