  While the state-of-the-art performance on entity resolution (ER) has been
achieved by deep learning, its effectiveness depends on large quantities of
accurately labeled training data. To alleviate the data labeling burden, Active
Learning (AL) presents itself as a feasible solution that focuses on data
deemed useful for model training. Building upon the recent advances in risk
analysis for ER, which can provide a more refined estimate on label
misprediction risk than the simpler classifier outputs, we propose a novel AL
approach of risk sampling for ER. Risk sampling leverages misprediction risk
estimation for active instance selection. Based on the core-set
characterization for AL, we theoretically derive an optimization model which
aims to minimize core-set loss with non-uniform Lipschitz continuity. Since the
defined weighted K-medoids problem is NP-hard, we then present an efficient
heuristic algorithm. Finally, we empirically verify the efficacy of the
proposed approach on real data by a comparative study. Our extensive
experiments have shown that it outperforms the existing alternatives by
considerable margins. Using ER as a test case, we demonstrate that risk
sampling is a promising approach potentially applicable to other challenging
classification tasks.
