@inproceedings{kartchner-etal-2023-comprehensive,
title = "A Comprehensive Evaluation of Biomedical Entity Linking Models",
author = "Kartchner, David and
Deng, Jennifer and
Lohiya, Shubham and
Kopparthi, Tejasri and
Bathala, Prasanth and
Domingo-Fern{\'a}ndez, Daniel and
Mitchell, Cassie",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.893",
doi = "10.18653/v1/2023.emnlp-main.893",
pages = "14462--14478",
abstract = "Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking",
}
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<abstract>Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking</abstract>
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%0 Conference Proceedings
%T A Comprehensive Evaluation of Biomedical Entity Linking Models
%A Kartchner, David
%A Deng, Jennifer
%A Lohiya, Shubham
%A Kopparthi, Tejasri
%A Bathala, Prasanth
%A Domingo-Fernández, Daniel
%A Mitchell, Cassie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kartchner-etal-2023-comprehensive
%X Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking
%R 10.18653/v1/2023.emnlp-main.893
%U https://aclanthology.org/2023.emnlp-main.893
%U https://doi.org/10.18653/v1/2023.emnlp-main.893
%P 14462-14478
Markdown (Informal)
[A Comprehensive Evaluation of Biomedical Entity Linking Models](https://aclanthology.org/2023.emnlp-main.893) (Kartchner et al., EMNLP 2023)
ACL
- David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, and Cassie Mitchell. 2023. A Comprehensive Evaluation of Biomedical Entity Linking Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14462–14478, Singapore. Association for Computational Linguistics.