  Pure machine-based solutions usually struggle in the challenging
classification tasks such as entity resolution (ER). To alleviate this problem,
a recent trend is to involve the human in the resolution process, most notably
the crowdsourcing approach. However, it remains very challenging to effectively
improve machine-based entity resolution with limited human effort. In this
paper, we investigate the problem of human and machine cooperation for ER from
a risk perspective. We propose to select the machine-labeled instances at high
risk of being mislabeled for manual verification. For this task, we present a
risk model that takes into consideration the human-labeled instances as well as
the output of machine resolution. Finally, we evaluate the performance of the
proposed risk model on real data. Our experiments demonstrate that it can pick
up the mislabeled instances with considerably higher accuracy than the existing
alternatives. Provided with the same amount of human cost budget, it can also
achieve better resolution quality than the state-of-the-art approach based on
active learning.
