BackgroundTree-structured neural networks have shown promise in extracting lexical representations of sentence syntactic structures, particularly in the detection of event triggers using recursive neural networks.MethodsIn this study, we introduce an attention mechanism intoChild-Sum Tree-LSTMsfor the detection of biomedical event triggers. We incorporate previous researches on assigning attention weights to adjacent nodes and integrate this mechanism intoChild-Sum Tree-LSTMsto improve the detection of event trigger words. We also address a limitation of shallow syntactic dependencies inChild-Sum Tree-LSTMsby integrating deep syntactic dependencies to enhance the effect of the attention mechanism.ResultsOur proposed model, which integrates an enhanced attention mechanism into Tree-LSTM, shows the best performance for the MLEE and BioNLP’09 datasets. Moreover, our model outperforms almost all complex event categories for the BioNLP’09/11/13 test set.ConclusionWe evaluate the performance of our proposed model with the MLEE and BioNLP datasets and demonstrate the advantage of an enhanced attention mechanism in detecting biomedical event trigger words.