During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted Change of F Measure will be introduced that attempts to provide the users an estimate of how much performance of the model is changing at each iteration. This stopping method can be applied with any base learner. This method is useful for reducing the data annotation bottleneck encountered when building text classification systems.
Active learning is a promising method to reduce human's effort for data annotation in different NLP applications. Since it is an iterative task, it should be stopped at some point which is optimum or near-optimum. In this paper we propose a novel stopping criterion for active learning of frame assignment based on the variability of the classifier's confidence score on the unlabeled data. The important advantage of this criterion is that we rely only on the unlabeled data to stop the data annotation process; as a result there are no requirements for the gold standard data and testing the classifier's performance in each iteration. Our experiments show that the proposed method achieves 93.67 of the classifier maximum performance.