Child_Sum EATree-LSTMs: Enhanced Attentive Child_Sum Tree-LSTMs for Biomedical Event Extraction

Abstract

Background The tree-structured neural network can deeply extract lexical representations of sentence syntactic structure. Some studies have utilized Recursive Neural Network to detect event triggers. Methods We incorporate the attention mechanism into Child-Sum Tree-LSTMs for the task of biomedical event triggers. Based on the previous research, we incorporated attention mechanism into Child-Sum Tree-LSTMs to assign an attention weight for the adjacent nodes to detect the biomedical event trigger words. The existing shallow syntactic dependencies in Child-Sum Tree-LSTMs ignore the deep syntactic dependencies. To enhance the effect of attention mechanism, we integrate the enhanced attention mechanism into the Child-Sum Tree-LSTMs model using the deep syntactic dependencies. Results Our proposed model integrating an enhanced the attention mechanism in Tree-LSTM on MLEE and BioNLP’09 both show best performance. The model also achieves the better performance on almost all of the complex event categories on the test set of BioNLP’09/11/13. Conclusion We evaluate the model performance on the MLEE and BioNLP datasets, and the experimental results demonstrate the advantage of enhanced attention to detect biomedical event trigger words.