Lin Shaofu, Xu Zhe, Sheng Ying, Chen Lihong, Chen Jianhui
Faculty of Information Technology, Beijing University of Technology, Beijing, China.
Beijing Institute of Smart City, Beijing University of Technology, Beijing, China.
Front Neurosci. 2022 Mar 7;15:739535. doi: 10.3389/fnins.2021.739535. eCollection 2021.
Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.
出处是神经影像资源共享的一个研究重点。已经开展了大量工作，以标准化且便捷的方式构建高质量的神经影像出处。然而，除了现有的基于处理过程的出处提取方法外，计算神经科学中的开放研究共享仍需要一种从快速增长的已发表资源中提取出处信息的方法。本文提出了一种基于文献挖掘的方法，用于面向研究共享的神经影像出处构建。定义了一组包含神经影像事件的属性来对神经影像研究的全过程进行建模，并提出了一种基于深度对抗学习的联合提取模型，称为AT-NeuroEAE，以在少样本学习场景中实现事件提取。最后，对来自《公共科学图书馆·综合》杂志的真实数据集进行了一组实验。实验结果表明，所提出的方法为面向开放研究共享的神经影像出处构建快速收集研究信息提供了一种实用方法。
