| Popis: |
This paper describes our submission to the BioCreative VII LitCovid track Multi-label topic classification for COVID-19 literature annotation. Our system generates embeddings for title, abstract, and keywords using the transformer-based pre-trained language model SciBERT. The classification layer consists of several multi-layer perceptrons, each predicting the applicability of a single label. Our approach, originally developed for hierarchical patent classification, shows a strong performance on the LitCovid shared task, outperforming roughly 75% of the participating systems. Keywords—document representation; multi-task learning; multi-label classification. |
