School of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230026, People's Republic of China.
Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, Huangshan Road, Hefei, 230026, People's Republic of China.
BMC Bioinformatics. 2020 Feb 6;21(1):47. doi: 10.1186/s12859-020-3376-2.
Biomedical event extraction is a fundamental and in-demand technology that has attracted substantial interest from many researchers. Previous works have heavily relied on manual designed features and external NLP packages in which the feature engineering is large and complex. Additionally, most of the existing works use the pipeline process that breaks down a task into simple sub-tasks but ignores the interaction between them. To overcome these limitations, we propose a novel event combination strategy based on hybrid deep neural networks to settle the task in a joint end-to-end manner.
We adapted our method to several annotated corpora of biomedical event extraction tasks. Our method achieved state-of-the-art performance with noticeable overall F1 score improvement compared to that of existing methods for all of these corpora.
The experimental results demonstrated that our method is effective for biomedical event extraction. The combination strategy can reconstruct complex events from the output of deep neural networks, while the deep neural networks effectively capture the feature representation from the raw text. The biomedical event extraction implementation is available online;http://www.
xin/event_extraction;http://2013.bionlp-st.org/tasks;http://nactem.ac.uk/MLEE/.Otherwise, please provide alternatives.
生物医学事件抽取是一项基础且需求旺盛的技术，吸引了众多研究人员的关注。之前的工作主要依赖于手动设计的特征和外部 NLP 包，其中特征工程庞大而复杂。此外，大多数现有工作使用流水线处理，将任务分解为简单的子任务，但忽略了它们之间的交互。为了克服这些限制，我们提出了一种新的基于混合深度神经网络的事件组合策略，以联合端到端的方式解决该任务。
我们将我们的方法应用于几个生物医学事件抽取任务的标注语料库。与现有方法相比，我们的方法在所有这些语料库中都取得了最先进的性能，整体 F1 得分有了显著提高。
实验结果表明，我们的方法对生物医学事件抽取是有效的。组合策略可以从深度神经网络的输出中重建复杂事件，而深度神经网络可以有效地从原始文本中捕获特征表示。生物医学事件抽取的实现可在网上获取；http://www.
xin/event_extraction;http://2013.bionlp-st.org/tasks;http://nactem.ac.uk/MLEE/.否则，请提供替代方案。
