# BioSentVec: creating sentence embeddings for biomedical texts

arXiv: https://arxiv.org/abs/1810.09302 · HTML: https://mlsanity.com/paper/1810.09302/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 30 of 47)

- [MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval](https://mlsanity.com/paper/2307.00589/) (2023)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
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- [CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes](https://mlsanity.com/paper/2106.02524/) (2021)
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- [Bioformer: an efficient transformer language model for biomedical text mining](https://mlsanity.com/paper/2302.01588/) (2023)
- [Extracting Adverse Drug Events from Clinical Notes](https://mlsanity.com/paper/2104.10791/) (2021)
- [How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation](https://mlsanity.com/paper/2201.04672/) (2022)
- [QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification](https://mlsanity.com/paper/2104.11572/) (2021)
- [Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios](https://mlsanity.com/paper/2211.05980/) (2022)
- [Automated Identification of Eviction Status from Electronic Health Record Notes](https://mlsanity.com/paper/2212.02762/) (2022)
- [Zero-Shot Multi-Label Topic Inference with Sentence Encoders](https://mlsanity.com/paper/2304.07382/) (2023)
- [A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data](https://mlsanity.com/paper/2405.12833/) (2024)
- [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025)
- [Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints](https://mlsanity.com/paper/2210.09440/) (2022)
- [PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder](https://mlsanity.com/paper/2505.24646/) (2025)
- [Filter Drug-induced Liver Injury Literature with Natural Language Processing and Ensemble Learning](https://mlsanity.com/paper/2203.11015/) (2022)
- [EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal](https://mlsanity.com/paper/2603.00028/) (2026)
- [Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation](https://mlsanity.com/paper/2311.16588/) (2023)
- [SciGisPy: a Novel Metric for Biomedical Text Simplification via Gist Inference Score](https://mlsanity.com/paper/2410.09632/) (2024)
- [Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories](https://mlsanity.com/paper/2208.01341/) (2022)
- [Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest](https://mlsanity.com/paper/2312.12989/) (2023)

## Abstract (arXiv)

Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods. Although pre-trained sentence encoders are available in the general domain, none exists for biomedical texts to date. In this work, we introduce BioSentVec: the first open set of sentence embeddings trained with over 30 million documents from both scholarly articles in PubMed and clinical notes in the MIMIC-III Clinical Database. We evaluate BioSentVec embeddings in two sentence pair similarity tasks in different text genres. Our benchmarking results demonstrate that the BioSentVec embeddings can better capture sentence semantics compared to the other competitive alternatives and achieve state-of-the-art performance in both tasks. We expect BioSentVec to facilitate the research and development in biomedical text mining and to complement the existing resources in biomedical word embeddings. BioSentVec is publicly available at https://github.com/ncbi-nlp/BioSentVec