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Zero/Few-shot classification of biomedical articles in context of the COVID-19 pandemic
at
| Simon Lupart, Benoit Favre, Vassilina Nikoulina, Salah Ait-Mokhtar |
| Workshop on Scientific Document Understanding at the 36th AAAI conference on artificial intelligence, virtual event, 1 March, 2022 |
| arXiv |
MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing publications in the biomedical domain. In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic. Zero-shot classification is an adequate response for timely labeling of the stream of papers with MeSH categories.
In this work we hypothesise that rich semantic information available in MeSH has potential to improve BioBERT representations and make them more suitable for zero-shot/few-shot tasks. We frame the problem as determining if MeSH term definitions, concatenated with paper abstracts are valid instances or not, and leverage multitask learning to induce the MeSH hierarchy in the representations.
Results establish a baseline on the MedLine and LitCovid datasets, and probing shows that the resulting representations convey the hierarchical relations present in MeSH.
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