Multi-label topic classification model of COVID-19 literature

Jieqiong Zheng

doi:10.1117/12.3004403

16 August 2023 Multi-label topic classification model of COVID-19 literature

Jieqiong Zheng

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870W (2023) https://doi.org/10.1117/12.3004403
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

One of the most significant aims of natural language processing is automatic knowledge extraction. The amount of COVID-19 literature is increasing by ten thousand each month, which greatly complicates manual annotation and downstream activities. In this paper, we describe a system for biomedical multic-label topic classification. Firstly, BERT is pre-trained on PMC and PubMed biomedical corpora which helps to capture deep semantic information. Additionally, we fine-tune the pre-trained BERT using the COVID-19 literature from the LitCovid Database. Finally, we predict the topic of LitCovid scientific literature using the novel model. The experimental results of our model on the BioCreative LitCovid corpus achieves a micro F-score of 91.14%, which is 1.29 percentage points higher than BERT. The F-scores of the our model are 1.33%, 2.32%, 0.27%, 0.44%, 6.91%, 14.14% higher than BERT on Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting topics respectively, which demonstrates the potential and effectiveness of the proposed framework.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jieqiong Zheng "Multi-label topic classification model of COVID-19 literature", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870W (16 August 2023); https://doi.org/10.1117/12.3004403

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