Abstract
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.
Highlights
Neuroimaging text mining is to extract knowledge from neuroimaging texts [1] and has received widespread attention
Sheng et al [4] designed the brand new neuroimaging named entity recognition task based on BI provenances and developed the deep learning-based method to recognize these entities for research sharing
3.3 Experimental results In the experiment, the proposed neuroimaging EventBTM and latent Dirichlet assignment (LDA) were trained based on the training set, and four baseline methods were performed on both the abstract test set and full-text test set
Summary
Neuroimaging text mining is to extract knowledge from neuroimaging texts [1] and has received widespread attention. Shardlow et al [2] combined active learning and deep learning to recognize various neuroscience entities for curating research information in computational neuroscience. Riedel et al [3] recognized various entities related to cognitive experiments based on multiple corpus features and classifiers. Sheng et al [4] designed the brand new neuroimaging named entity recognition task based on BI provenances and developed the deep learning-based method to recognize these entities for research sharing. These studies can only extract valuable information from neuroimaging literature and cannot locate the research focus in literature.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
