An event based topic learning pipeline for neuroimaging literature mining

Chen, L, Yan, J, Chen, J, Sheng, Y, Xu, Z and Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348, 2020. An event based topic learning pipeline for neuroimaging literature mining. Brain Informatics, 7 (1): 18. ISSN 2198-4018

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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.

Item Type:	Journal article
Publication Title:	Brain Informatics
Creators:	Chen, L., Yan, J., Chen, J., Sheng, Y., Xu, Z. and Mahmud, M.
Publisher:	Springer Science and Business Media LLC
Date:	December 2020
Volume:	7
Number:	1
ISSN:	2198-4018
Identifiers:	Number Type 10.1186/s40708-020-00121-1 DOI 1390425 Other
Rights:	© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	25 Nov 2020 08:51
Last Modified:	31 May 2021 15:13
URI:	https://irep.ntu.ac.uk/id/eprint/41686

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