natural-language-processing

Sentence Embeddings For Biomedical Texts

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

BioSentVec: creating sentence embeddings for biomedical texts

Zhiyong Lu, Yifan Peng, Qingyu Chen•Sun Oct 21 2018

This work introduces 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 MIMICIII Clinical Database and expects it to facilitate the research and development in biomedical text mining and to complement the existing resources in biomedical word embeddins.

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Neural sentence embedding models for semantic similarity estimation in the biomedical domain

Kathrin Blagec, Hong Xu, Asan Agibetov, M. Samwald•Wed Apr 10 2019

The value of neural network-based models for semantic similarity estimation in the biomedical domain is highlighted by showing that they can keep up with and even surpass previous state-of-the-art approaches for semantic similarities estimation, when evaluated on a biomedical benchmark set.

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