# A Lightweight Neural Model for Biomedical Entity Linking

arXiv: https://arxiv.org/abs/2012.08844 · HTML: https://mlsanity.com/paper/2012.08844/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [What is the Role of Small Models in the LLM Era: A Survey](https://mlsanity.com/paper/2409.06857/) (2024)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (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)
- [Learn to Not Link: Exploring NIL Prediction in Entity Linking](https://mlsanity.com/paper/2305.15725/) (2023)
- [BELB: a Biomedical Entity Linking Benchmark](https://mlsanity.com/paper/2308.11537/) (2023)
- [BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation](https://mlsanity.com/paper/2401.05125/) (2024)
- [Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization](https://mlsanity.com/paper/2308.12025/) (2023)
- [Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints](https://mlsanity.com/paper/2509.01899/) (2025)
- [Bridging Lexical Divergence: LLM-Assisted, Cost-Efficient, Zero-shot Scientific Entity Linking](https://mlsanity.com/paper/2609.00228/) (2026)

## Abstract (arXiv)

Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word orderings. Recently, BERT-based methods have advanced the state-of-the-art by allowing for rich representations of word sequences. However, they often have hundreds of millions of parameters and require heavy computing resources, which limits their applications in resource-limited scenarios. Here, we propose a lightweight neural method for biomedical entity linking, which needs just a fraction of the parameters of a BERT model and much less computing resources. Our method uses a simple alignment layer with attention mechanisms to capture the variations between mention and entity names. Yet, we show that our model is competitive with previous work on standard evaluation benchmarks.