Fast and Effective Biomedical Entity Linking Using a Dual Encoder

Rajarshi Bhowmik, Karl Stratos, Gerard de Melo


Abstract
Biomedical entity linking is the task of identifying mentions of biomedical concepts in text documents and mapping them to canonical entities in a target thesaurus. Recent advancements in entity linking using BERT-based models follow a retrieve and rerank paradigm, where the candidate entities are first selected using a retriever model, and then the retrieved candidates are ranked by a reranker model. While this paradigm produces state-of-the-art results, they are slow both at training and test time as they can process only one mention at a time. To mitigate these issues, we propose a BERT-based dual encoder model that resolves multiple mentions in a document in one shot. We show that our proposed model is multiple times faster than existing BERT-based models while being competitive in accuracy for biomedical entity linking. Additionally, we modify our dual encoder model for end-to-end biomedical entity linking that performs both mention span detection and entity disambiguation and out-performs two recently proposed models.
Anthology ID:
2021.louhi-1.4
Volume:
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Month:
April
Year:
2021
Address:
online
Venue:
Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–37
Language:
URL:
https://aclanthology.org/2021.louhi-1.4
DOI:
Bibkey:
Cite (ACL):
Rajarshi Bhowmik, Karl Stratos, and Gerard de Melo. 2021. Fast and Effective Biomedical Entity Linking Using a Dual Encoder. In Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pages 28–37, online. Association for Computational Linguistics.
Cite (Informal):
Fast and Effective Biomedical Entity Linking Using a Dual Encoder (Bhowmik et al., Louhi 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.louhi-1.4.pdf
Code
 kingsaint/BioMedical-EL
Data
BC5CDRMedMentions