Adaptive deep learning for entity resolution by risk analysis

The state-of-the-art performance on entity resolution (ER) has been achieved by deep learning. However, deep models usually need to be trained on large quantities of accurately labeled training data, and cannot be easily tuned towards a target workload. However, in real scenarios, there may not be sufficient training data; even if they are abundant, their distribution is almost certainly different from target data to some extent.