BioSentVec: creating sentence embeddings for biomedical texts

Qingyu Chen, Yifan Peng, Zhiyong Lu
10/22/2018
cs.CLcs.AIcs.LG

Official code: ncbi-nlp/BioSentVec (615 stars, last commit 2023-08-15). 1 more implementation below.

Abstract

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 Implementations(2)

ncbi-nlp/BioSentVecOfficial100%

BioWordVec & BioSentVec: pre-trained embeddings for biomedical words and sentences

615100Shell, PythonSep 19, 20183 years agoNOASSERTION
bionlpfasttextmimic-iiinatural-language-processingpubmed+4 more
ncbi-nlp/BioWordVecCommunity100%
15330PythonFeb 22, 20192 years agoNOASSERTION

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Cite this paper

@article{chen2018biosentvec,
  title  = {BioSentVec: creating sentence embeddings for biomedical texts},
  author = {Qingyu Chen and Yifan Peng and Zhiyong Lu},
  year   = {2018},
  eprint = {1810.09302},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1810.09302}
}

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