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- W4385877648 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." @default.
- W4385877648 created "2023-08-17" @default.
- W4385877648 creator A5050899124 @default.
- W4385877648 date "2023-08-16" @default.
- W4385877648 modified "2023-10-04" @default.
- W4385877648 title "Multi-label topic classification model of COVID-19 literature" @default.
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- W4385877648 doi "https://doi.org/10.1117/12.3004403" @default.
- W4385877648 hasPublicationYear "2023" @default.
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