Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic
2022 Workshop on Scientific Document Understanding, SDU 2022
; 3164, 2022.
Article
in English
| Scopus | ID: covidwho-1958163
ABSTRACT
MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of 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 s are valid instances or not, and leverage multi-task learning to induce the MeSH hierarchy in the representations thanks to a seq2seq task. Results establish a baseline on the MedLine and LitCovid datasets, and probing shows that the resulting representations convey the hierarchical relations present in MeSH. © 2021 Copyright for this paper by its authors.
Healthcare Medicine & Wellness; Text Classification; Transfer Domain Adaptation Multi-Task Learning; Classification (of information); Learning systems; Mesh generation; Semantics; Text processing; Zero-shot learning; Domain adaptation; In contexts; Medical subject headings; Multitask learning; National library of medicines; Shot classification; Transfer domains; COVID-19
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 Workshop on Scientific Document Understanding, SDU 2022
Year:
2022
Document Type:
Article
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