Event detection suffers from data sparseness and label imbalance prob-lem due to the expensive cost of manual annotations of events.To address this problem,we propose a novel approach that allows for information sharing among related event types.Specifically,we employ a fully connected three-layer artifi-cial neural network as our basic model and propose a type-group regularization term to achieve the goal of information sharing.We conduct experiments with different configurations of type groups,and the experimental results show that in-formation sharing among related event types remarkably improves the detecting performance.Compared with state-of-the-art methods,our proposed approach achieves a better F1 score on the widely used ACE 2005 event evaluation dataset.