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SWACG: A Hybrid Neural Network Integrating Sliding Window for Biomedical Event Trigger Extraction


 As an important part of biomedical text mining, biomedical events play a key role in improving biomedical research and disease prevention. Trigger identification, extracting the words describing the event types, is a critical and prerequisite step for biomedical event extraction. Traditional methods excessively rely on natural language processing tools in the feature extraction process, incurring a significant manual cost. In addition, because of the particularity of the biomedical literature, the problem of long-distance dependency is obvious. To solve these problems, we propose a hybrid structure SWACG, which consists of the ReCNN-BiGRU (Residual CNN and Bidirectional Gated Recurrent Unit) hybrid neural network and MH-attention (Multi-Head attention) mechanism. The proposed model uses ReCNN to extract vocabulary-level features and BiGRU to obtain contextual semantic information. Furthermore, sliding window divides long sentences into equal-length short sentences without destroying context information, which can avoid long-distance dependency. Experimental results show that our method advances the state-of-the-art performance on the commonly used Multi-Level Event Extraction (MLEE) corpus, achieving 82.20% F-score.
