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.

中文翻译：

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基于事件的主题学习管道，用于神经影像文献挖掘

神经影像文本挖掘从神经影像文本中提取知识，受到了广泛的关注。主题学习是神经成像文本挖掘的重要研究重点。然而，当前的神经影像话题学习研究主要使用传统的概率话题模型从文献中提取话题，无法获得高质量的神经影像话题。现有的主题学习方法也不能满足针对全文神经影像文献的主题学习的要求。本文定义了三种类型的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Neuroimaging Event-BTM，以实现从全文神经成像文献中进行主题学习。