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Brain Imaging

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An event based topic learning pipeline for neuroimaging literature mining.

Lihong Chen^1,2, Jianzhuo Yan^1,2, Jianhui Chen^3,4

¹Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.

Brain Informatics

|November 23, 2020

Summary

This study introduces neuroimaging Event-BTM, an event-based topic learning method for full-text neuroimaging literature. It significantly improves topic extraction accuracy and completeness compared to traditional models.

Keywords:

Biterm topic model Event extraction Neuroimaging text mining Topic learning

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Area of Science:

Neuroscience
Computational Linguistics
Data Science

Background:

Neuroimaging text mining is crucial for knowledge extraction.
Traditional topic models struggle with high-quality topic extraction from full-text neuroimaging literature.
Existing methods do not meet the demands of full-text neuroimaging literature analysis.

Purpose of the Study:

To define neuroimaging research topic events to describe research processes and outcomes.
To propose an event-based topic learning pipeline, neuroimaging Event-BTM, for full-text literature.
To enhance the quality and relevance of topics extracted from neuroimaging research.

Main Methods:

Defined three types of neuroimaging research topic events.
Developed the neuroimaging Event-BTM pipeline for event-based topic learning.
Utilized a full-text literature dataset (Plos One) for evaluation.

Main Results:

The proposed neuroimaging Event-BTM method significantly outperforms existing topic learning approaches.
Demonstrated superior accuracy in extracting neuroimaging topics.
Showcased enhanced completeness in topic representation from full-text literature.

Conclusions:

Neuroimaging Event-BTM offers a more effective approach for topic learning in neuroimaging.
Event-based topic modeling is a promising direction for analyzing scientific literature.
The method provides higher quality and more comprehensive neuroimaging topics.