REAL: A Retrieval-Augmented Entity Linking Approach for Biomedical Concept Recognition

Darya Shlyk, Tudor Groza, Marco Mesiti, Stefano Montanelli, Emanuele Cavalleri

Abstract
Large Language Models (LLMs) offer an appealing alternative to training dedicated models for many Natural Language Processing (NLP) tasks. However, outdated knowledge and hallucination issues can be major obstacles in their application in knowledge-intensive biomedical scenarios. In this study, we consider the task of biomedical concept recognition (CR) from unstructured scientific literature and explore the use of Retrieval Augmented Generation (RAG) to improve accuracy and reliability of the LLM-based biomedical CR. Our approach, named REAL (Retrieval Augmented Entity Linking), combines the generative capabilities of LLMs with curated knowledge bases to automatically annotate natural language texts with concepts from bio-ontologies. By applying REAL to benchmark corpora on phenotype concept recognition, we show its effectiveness in improving LLM-based CR performance. This research highlights the potential of combining LLMs with external knowledge sources to advance biomedical text processing.
Anthology ID:
2024.bionlp-1.29
Volume:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
380–389
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2024.bionlp-1.29/
DOI:
10.18653/v1/2024.bionlp-1.29
Bibkey:
Cite (ACL):
Darya Shlyk, Tudor Groza, Marco Mesiti, Stefano Montanelli, and Emanuele Cavalleri. 2024. REAL: A Retrieval-Augmented Entity Linking Approach for Biomedical Concept Recognition. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 380–389, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
REAL: A Retrieval-Augmented Entity Linking Approach for Biomedical Concept Recognition (Shlyk et al., BioNLP 2024)
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PDF:
https://preview.aclanthology.org/icon-24-ingestion/2024.bionlp-1.29.pdf
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