# Fast and Effective Biomedical Entity Linking Using a Dual Encoder

arXiv: https://arxiv.org/abs/2103.05028 · HTML: https://mlsanity.com/paper/2103.05028/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking](https://mlsanity.com/paper/2005.14253/) (2020)
- [LATTE: Latent Type Modeling for Biomedical Entity Linking](https://mlsanity.com/paper/1911.09787/) (2019)
- [Parallel sequence tagging for concept recognition](https://mlsanity.com/paper/2003.07424/) (2020)

## Built on by (most cited first, 16 of 16)

- [Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning](https://mlsanity.com/paper/2204.05164/) (2022)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text](https://mlsanity.com/paper/2110.08228/) (2021)
- [Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model](https://mlsanity.com/paper/2111.09634/) (2021)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking](https://mlsanity.com/paper/2302.07189/) (2023)
- [Learning from Negative Samples in Biomedical Generative Entity Linking](https://mlsanity.com/paper/2408.16493/) (2024)
- [Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining](https://mlsanity.com/paper/2303.15311/) (2023)
- [CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs](https://mlsanity.com/paper/2312.08036/) (2023)
- [xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization](https://mlsanity.com/paper/2310.11275/) (2023)
- [Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval](https://mlsanity.com/paper/2301.13318/) (2023)
- [Exploring Partial Knowledge Base Inference in Biomedical Entity Linking](https://mlsanity.com/paper/2303.10330/) (2023)
- [Biomedical Entity Linking with Triple-aware Pre-Training](https://mlsanity.com/paper/2308.14429/) (2023)
- [Knowledge-Aware Neural Networks for Medical Forum Question Classification](https://mlsanity.com/paper/2109.13141/) (2021)
- [Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization](https://mlsanity.com/paper/2305.14645/) (2023)
- [Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation](https://mlsanity.com/paper/2407.02719/) (2024)

## Abstract (arXiv)

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.