KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

Xindi Wang, Robert Mercer, Frank Rudzicz


Abstract
Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information. With the rapid growth of the PubMed database, large-scale biomedical document indexing becomes increasingly important. MeSH indexing is a challenging task for machine learning, as it needs to assign multiple labels to each article from an extremely large hierachically organized collection. To address this challenge, we propose KenMeSH, an end-to-end model that combines new text features and a dynamic knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.
Anthology ID:
2022.acl-long.210
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2941–2951
Language:
URL:
https://aclanthology.org/2022.acl-long.210
DOI:
10.18653/v1/2022.acl-long.210
Bibkey:
Cite (ACL):
Xindi Wang, Robert Mercer, and Frank Rudzicz. 2022. KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2941–2951, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling (Wang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.210.pdf
Code
 xdwang0726/kenmesh