Biomedical Event Extraction Based on Distributed Representation and Deep Learning

The two main problems of biomedical event extraction are trigger identification and argument detection which can both be considered as classification problems. In this paper, we propose a distributed representation method, which combines context, consisted by dependency-based word embedding, and task-based features represented in a distributed way on deep learning models to realize biomedical event extraction. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores compared to the state-of-the-art SVM method. This demonstrates that our proposed method is effective for biomedical event extraction.