A deep learning relation extraction approach to support a biomedical semi-automatic curation task: The case of the gluten bibliome
作者：
Highlights：
• A semi-automatic workflow to curate health-related interactions in the bibliome.
• Different text minning an ontology-based methods were combined and applied.
• A deep learning model to assist manual curators learning from their decisions.
• Different state-of-the-art machine learning models were compared.
• Results showed that the presented workflow is a valuable approach to assist curators.
摘要
•A semi-automatic workflow to curate health-related interactions in the bibliome.•Different text minning an ontology-based methods were combined and applied.•A deep learning model to assist manual curators learning from their decisions.•Different state-of-the-art machine learning models were compared.•Results showed that the presented workflow is a valuable approach to assist curators.
论文关键词：Text mining,Relation extraction,Deep learning,Ontology-based methods,Literature curation,Gluten
论文评审过程：Received 13 July 2021, Revised 22 September 2021, Accepted 24 January 2022, Available online 1 February 2022, Version of Record 7 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116616
