Papers › BioSentVec: creating sentence embeddings for biomedical texts
BioSentVec: creating sentence embeddings for biomedical texts
Qingyu Chen, Yifan Peng, Zhiyong Lu
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
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentence Embeddings For Biomedical Texts | BIOSSES | BioSentVec (PubMed) | Pearson Correlation | 0.817 | #4 of 14 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | BIOSSES | BioSentVec (PubMed + MIMIC-III) | Pearson Correlation | 0.795 | #7 of 14 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | BIOSSES | BioSentVec (MIMIC-III) | Pearson Correlation | 0.350 | #12 of 14 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | BIOSSES | Universal Sentence Encoder | Pearson Correlation | 0.345 | #13 of 14 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | MedSTS | BioSentVec (PubMed + MIMIC-III) | Pearson Correlation | 0.767 | #1 of 4 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | MedSTS | BioSentVec (MIMIC-III) | Pearson Correlation | 0.759 | #2 of 4 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | MedSTS | BioSentVec (PubMed) | Pearson Correlation | 0.750 | #3 of 4 | Archive leaderboard | report |
| Sentence Embeddings For Biomedical Texts | MedSTS | Universal Sentence Encoder | Pearson Correlation | 0.714 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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