# Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations

arXiv: https://arxiv.org/abs/2204.00391 · HTML: https://mlsanity.com/paper/2204.00391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [CODER: Knowledge infused cross-lingual medical term embedding for term normalization](https://mlsanity.com/paper/2011.02947/) (2020)
- [BIOS: An Algorithmically Generated Biomedical Knowledge Graph](https://mlsanity.com/paper/2203.09975/) (2022)

## Built on by (most cited first, 5 of 5)

- [CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs](https://mlsanity.com/paper/2312.08036/) (2023)
- [Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language](https://mlsanity.com/paper/2211.05035/) (2022)
- [Hierarchical Pretraining for Biomedical Term Embeddings](https://mlsanity.com/paper/2307.00266/) (2023)
- [DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data](https://mlsanity.com/paper/2507.18583/) (2025)
- [Automated Hierarchical Graph Construction for Multi-source Electronic Health Records](https://mlsanity.com/paper/2509.06576/) (2025)

## Abstract (arXiv)

Term clustering is important in biomedical knowledge graph construction. Using similarities between terms embedding is helpful for term clustering. State-of-the-art term embeddings leverage pretrained language models to encode terms, and use synonyms and relation knowledge from knowledge graphs to guide contrastive learning. These embeddings provide close embeddings for terms belonging to the same concept. However, from our probing experiments, these embeddings are not sensitive to minor textual differences which leads to failure for biomedical term clustering. To alleviate this problem, we adjust the sampling strategy in pretraining term embeddings by providing dynamic hard positive and negative samples during contrastive learning to learn fine-grained representations which result in better biomedical term clustering. We name our proposed method as CODER++, and it has been applied in clustering biomedical concepts in the newly released Biomedical Knowledge Graph named BIOS.