A Novel Method for Multiple Biomedical Events Extraction with Reinforcement Learning and Knowledge Bases
Weizhong Zhao, Yao Zhao, Xingpeng Jiang, Tingting He, Fan Liu, Ning Li · 2020
Biomedical event extraction is usually modeled as two sub-tasks: trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially but ignore the interaction between them. This paper proposes a novel method for multiple biomedical events extraction, in which the task of event extraction is modeled under a framework of reinforcement learning (RL). We treat the trigger identification and argument detection as main-task and subsidiary-task, respectively. And the result of argument detection is modeled as environmental information. In this way, the proposed method can capture the interaction between two sub-tasks, and the semantic associations among multiple biomedical events are also utilized effectively. Moreover, external biomedical knowledge bases are employed for representation learning of biomedical text. Comprehensive experiments are conducted on two widely used biomedical corpora, and results demonstrate that our method gains better performance compared to existing methods, especially in multiple biomedical events extraction.