Weizhong Zhao1,2,3,4,5, Yao Zhao1,2,3, Xingpeng Jiang1,2,3, Tingting He1,2,3, Fan Liu6, Ning Li7. 1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China. 2. School of Computer, Central China Normal University, Wuhan, China. 3. National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, PR China. 4. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China. 5. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, China. 6. Comcast Silicon Valley Innovation Center, Sunnyvale, CA, USA. 7. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China.
Abstract
MOTIVATION: Multiple events extraction from biomedical literature is a challenging task for biomedical community. Usually, biomedical event extraction is modeled as two sub-tasks, trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially, and fail to make full use of the interaction between them, leading to suboptimal results for multiple biomedical events extraction. METHOD: We propose a novel framework of reinforcement learning (RL) for the task of multiple biomedical events extraction. More specifically, trigger identification and argument detection are treated as main-task and subsidiary-task, respectively. Assigning the event type of triggers (in the main-task) is viewed as the action taken in RL, and the result of corresponding argument detection (i.e. the subsidiary-task) for the identified trigger is used for computing the reward of the taken action. Moreover, the result of the subsidiary-task is modeled as part of environment information in RL to help the procedure of trigger identification. In addition, external biomedical knowledge bases are employed for representation learning of biomedical text, which can improve the performance of biomedical event extraction. RESULTS: Results on two widely used biomedical corpora demonstrate that the proposed framework performs better than the selected baselines on the task of multiple events extraction. The ablation test indicates the contributions of RL and external KBs to the performance improvement in the proposed method. In addition, by modeling multiple events extraction under the RL framework, the supervised information is exploited more effectively than the classical supervised learning paradigm.
MOTIVATION: Multiple events extraction from biomedical literature is a challenging task for biomedical community. Usually, biomedical event extraction is modeled as two sub-tasks, trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially, and fail to make full use of the interaction between them, leading to suboptimal results for multiple biomedical events extraction. METHOD: We propose a novel framework of reinforcement learning (RL) for the task of multiple biomedical events extraction. More specifically, trigger identification and argument detection are treated as main-task and subsidiary-task, respectively. Assigning the event type of triggers (in the main-task) is viewed as the action taken in RL, and the result of corresponding argument detection (i.e. the subsidiary-task) for the identified trigger is used for computing the reward of the taken action. Moreover, the result of the subsidiary-task is modeled as part of environment information in RL to help the procedure of trigger identification. In addition, external biomedical knowledge bases are employed for representation learning of biomedical text, which can improve the performance of biomedical event extraction. RESULTS: Results on two widely used biomedical corpora demonstrate that the proposed framework performs better than the selected baselines on the task of multiple events extraction. The ablation test indicates the contributions of RL and external KBs to the performance improvement in the proposed method. In addition, by modeling multiple events extraction under the RL framework, the supervised information is exploited more effectively than the classical supervised learning paradigm.