@inproceedings{hogan-etal-2021-blar,
title = "{BLAR}: Biomedical Local Acronym Resolver",
author = "Hogan, William and
Vazquez Baeza, Yoshiki and
Katsis, Yannis and
Baldwin, Tyler and
Kim, Ho-Cheol and
Hsu, Chun-Nan",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bionlp-1.14/",
doi = "10.18653/v1/2021.bionlp-1.14",
pages = "126--130",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T BLAR: Biomedical Local Acronym Resolver
%A Hogan, William
%A Vazquez Baeza, Yoshiki
%A Katsis, Yannis
%A Baldwin, Tyler
%A Kim, Ho-Cheol
%A Hsu, Chun-Nan
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 20th Workshop on Biomedical Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F hogan-etal-2021-blar
%X 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.
%R 10.18653/v1/2021.bionlp-1.14
%U https://aclanthology.org/2021.bionlp-1.14/
%U https://doi.org/10.18653/v1/2021.bionlp-1.14
%P 126-130
Markdown (Informal)
[BLAR: Biomedical Local Acronym Resolver](https://aclanthology.org/2021.bionlp-1.14/) (Hogan et al., BioNLP 2021)
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.