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            <title>Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis</title>
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            <namePart type="given">Antonio</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Fast and Effective Biomedical Entity Linking Using a Dual Encoder
%A Bhowmik, Rajarshi
%A Stratos, Karl
%A de Melo, Gerard
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
%D 2021
%8 April
%I Association for Computational Linguistics
%C online
%F bhowmik-etal-2021-fast
%X 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.
%U https://aclanthology.org/2021.louhi-1.4
%P 28-37

Markdown (Informal)

[Fast and Effective Biomedical Entity Linking Using a Dual Encoder](https://aclanthology.org/2021.louhi-1.4) (Bhowmik et al., Louhi 2021)

• Fast and Effective Biomedical Entity Linking Using a Dual Encoder (Bhowmik et al., Louhi 2021)

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.