He Xinyu, Tang Yujie, Yu Bo, Li Shixin, Ren Yonggong
IEEE/ACM Trans Comput Biol Bioinform. 2024 Nov-Dec;21(6):2064-2075. doi: 10.1109/TCBB.2024.3442199. Epub 2024 Dec 10.
Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect simple biomedical events and complex events with the same model, and the performance of the complex biomedical event extraction is relatively low. In this paper, we build different neural networks for simple and complex events respectively, which helps to promote the performance of complex event extraction. To avoid redundant information, we design dynamic path planning strategy for argument detection. To take full use of the information between the trigger identification and argument detection subtasks, and reduce the cascading errors, we build a joint event extraction model. Experimental results demonstrate our approach achieves the best F-score on the biomedical benchmark MLEE dataset and outperforms the recent state-of-the-art methods.
生物医学事件检测是分子生物学和生物医学研究中的一项关键信息提取任务，它为医学搜索、疾病预防和新药开发提供了启发。现有方法通常使用相同的模型来检测简单生物医学事件和复杂事件，而复杂生物医学事件提取的性能相对较低。在本文中，我们分别为简单事件和复杂事件构建了不同的神经网络，这有助于提高复杂事件提取的性能。为避免冗余信息，我们为论元检测设计了动态路径规划策略。为充分利用触发词识别和论元检测子任务之间的信息，并减少级联错误，我们构建了一个联合事件提取模型。实验结果表明，我们的方法在生物医学基准MLEE数据集上取得了最佳F值，优于最近的先进方法。
