# Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets

arXiv: https://arxiv.org/abs/2005.00460 · HTML: https://mlsanity.com/paper/2005.00460/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 9 of 9)

- [GREEN: Generative Radiology Report Evaluation and Error Notation](https://mlsanity.com/paper/2405.03595/) (2024)
- [Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text](https://mlsanity.com/paper/2110.08228/) (2021)
- [Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification](https://mlsanity.com/paper/2112.00733/) (2021)
- [Low-Rank Subspaces for Unsupervised Entity Linking](https://mlsanity.com/paper/2104.08737/) (2021)
- [MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction](https://mlsanity.com/paper/2204.04779/) (2022)
- [From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained](https://mlsanity.com/paper/2312.06188/) (2023)
- [Measuring Context-Word Biases in Lexical Semantic Datasets](https://mlsanity.com/paper/2112.06733/) (2021)
- [Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT Model on an Automatically Generated Wikipedia Corpus](https://mlsanity.com/paper/2405.11941/) (2024)
- [SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking](https://mlsanity.com/paper/2601.19667/) (2026)

## Abstract (arXiv)

Medical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text. Most of the existing methods adopt a three-step approach of (1) detecting mentions, (2) generating a list of candidate concepts, and finally (3) picking the best concept among them. In this paper, we probe into alleviating the problem of overgeneration of candidate concepts in the candidate generation module, the most under-studied component of medical entity linking. For this, we present MedType, a fully modular system that prunes out irrelevant candidate concepts based on the predicted semantic type of an entity mention. We incorporate MedType into five off-the-shelf toolkits for medical entity linking and demonstrate that it consistently improves entity linking performance across several benchmark datasets. To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance. We make our source code and datasets publicly available for medical entity linking research.