A context-enhanced neural network model for biomedical event trigger detection

As an important component of biomedical event extraction, biomedical event trigger detection has received extensive research attention in recent years. Most studies focus on designing various models or features according to the original text itself, but fail to leverage contextual information of the original text from external knowledge base such as Wikipedia, which is publicly available. To address the issue, we propose a context-enhanced neural network model that automatically integrates the related information from external knowledge base for biomedical event trigger detection. Specifically, the proposed model first extracts the related context of the original text from external knowledge base. Then the original text and its context are sequentially fed into the BERT embedding layer and Transformer convolution layer to learn high-level semantic representation. Finally, the probability of possible tags is calculated using the CRF layer. Experimental results on the MLEE dataset show our proposed model achieves 86.83% F1 score, outperforming the existing methods and context-enhanced baseline systems significantly. Experimental analysis also indicates the effectiveness of contextual information for trigger detection in biomedical domain.