Child-Sum EATree-LSTMs: enhanced attentive Child-Sum Tree-LSTMs for biomedical event extraction

BACKGROUND: Tree-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. METHODS: In this study, we introduce an attention mechanism into Child-Sum Tree-LSTMs for the detection of biomedical event triggers. We incorporate previous researches on assigning attention weights to adjacent nodes and integrate this mechanism into Child-Sum Tree-LSTMs to improve the detection of event trigger words. We also address a limitation of shallow syntactic dependencies in Child-Sum Tree-LSTMs by integrating deep syntactic dependencies to enhance the effect of the attention mechanism. RESULTS: Our 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. CONCLUSION: We 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.