An Integrated Biomedical Event Trigger Identification Approach With a Neural Network and Weighted Extreme Learning Machine

Biomedical event trigger identification is a sub-task in biomedical event extraction that aims to recognize the trigger label of biomedical events in context. It is a fundamental task in natural language processing. Previous approaches usually depended on feature engineering with unbalanced data. In this paper, we present a bidirectional long short-term memory convolution neural network weighted extreme learning machine (BC-WELM) to identify the biomedical event trigger. Using the different dimensions of embeddings as input, this model considers the contextual modeling by the Bi-LSTM and the local modeling by CNN and, then, classifies the trigger label to settle the unbalanced problem by the WELM. With this design, the BC-WELM model is helpful for biomedical event trigger identification. The experimental results on the MLEE dataset demonstrate that our approach is capable of outperforming the state-of-the-art baselines on an F1 score.