Chen Lihong, Yan Jianzhuo, Chen Jianhui, Sheng Ying, Xu Zhe, Mahmud Mufti
Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China.
Brain Inform. 2020 Nov 23;7(1):18. doi: 10.1186/s40708-020-00121-1.
Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods.
神经影像文本挖掘从神经影像文本中提取知识，受到了广泛关注。主题学习是神经影像文本挖掘的一个重要研究重点。然而，当前的神经影像主题学习研究主要使用传统概率主题模型从文献中提取主题，无法获得高质量的神经影像主题。现有的主题学习方法也无法满足面向神经影像全文文献的主题学习需求。本文定义了三种神经影像研究主题事件来描述神经影像研究的过程和结果。提出了一种基于事件的主题学习管道，称为神经影像事件 - BTM，以实现从神经影像全文文献中进行主题学习。在PLoS One数据集上的实验结果表明，该方法的准确性和完整性明显优于现有的主要主题学习方法。
