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bioRxiv · 10.1101/2021.10.26.465946

Multi-label topic classification for COVID-19 literature annotation using an ensemble model based on PubMedBERT

Tian, S.·Zhang, J.
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Abstract

The BioCreative VII Track 5 calls for participants to tackle the multi-label classification task for automated topic annotation of COVID-19 literature. In our participation, we evaluated several deep learning models built on PubMedBERT, a pre-trained language model, with different strategies addressing the challenges of the task. Specifically, multi-instance learning was used to deal with the large variation in the lengths of the articles, and focal loss function was used to address the imbalance in the distribution of different topics. We found that the ensemble model performed the best among all the models we have tested. Test results of our submissions showed that our approach was able to achieve satisfactory performance with an F1 score of 0.9247, which is significantly better than the baseline model (F1 score: 0.8678) and the mean of all the submissions (F1 score: 0.8931).

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Tian, S., Zhang, J.. 2021-10-29. Multi-label topic classification for COVID-19 literature annotation using an ensemble model based on PubMedBERT. https://doi.org/10.1101/2021.10.26.465946

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