A Two-Stage Biomedical Event Trigger Detection Method Based on Hybrid Neural Network and Sentence Embeddings

Biomedical event extraction is a challenging task in biomedical text mining, which plays an important role in improving biomedical research and disease prevention. As the crucial and prerequisite step in event extraction, biomedical trigger detection has attracted much attention. Previous approaches usually depended on feature engineering with unbalanced data. In this paper, we propose a two-stage method based on hybrid neural network for trigger detection, which divides trigger detection into recognition stage and classification stage. In the first stage, we build a BiLSTM based recognition model integrating attention mechanism (Att-BiLSTM). In the second stage, the classification model based on Passive-Aggressive online algorithm is constructed. Furthermore, to enrich sentence-level features, we establish sentence embeddings and add reading gate. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 80.26%, which achieves the state-of-the-art systems.