BLAR: Biomedical Local Acronym Resolver

William Hogan, Yoshiki Vazquez Baeza, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Chun-Nan Hsu


Abstract
NLP has emerged as an essential tool to extract knowledge from the exponentially increasing volumes of biomedical texts. Many NLP tasks, such as named entity recognition and named entity normalization, are especially challenging in the biomedical domain partly because of the prolific use of acronyms. Long names for diseases, bacteria, and chemicals are often replaced by acronyms. We propose Biomedical Local Acronym Resolver (BLAR), a high-performing acronym resolver that leverages state-of-the-art (SOTA) pre-trained language models to accurately resolve local acronyms in biomedical texts. We test BLAR on the Ab3P corpus and achieve state-of-the-art results compared to the current best-performing local acronym resolution algorithms and models.
Anthology ID:
2021.bionlp-1.14
Volume:
Proceedings of the 20th Workshop on Biomedical Language Processing
Month:
June
Year:
2021
Address:
Online
Venues:
BioNLP | NAACL
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–130
Language:
URL:
https://aclanthology.org/2021.bionlp-1.14
DOI:
10.18653/v1/2021.bionlp-1.14
Bibkey:
Cite (ACL):
William Hogan, Yoshiki Vazquez Baeza, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, and Chun-Nan Hsu. 2021. BLAR: Biomedical Local Acronym Resolver. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 126–130, Online. Association for Computational Linguistics.
Cite (Informal):
BLAR: Biomedical Local Acronym Resolver (Hogan et al., BioNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.bionlp-1.14.pdf