Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
TL
TL;DR
The Biomedical Language Understanding Evaluation (BLUE) benchmark is introduced to facilitate research in the development of pre-training language representations in the biomedicine domain and it is found that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results.
Abstract
Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We make the datasets, pre-trained models, and codes publicly available at https://github.com/ ncbi-nlp/BLUE_Benchmark.
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BioWordVec, improving biomedical word embeddings with subword information and MeSH
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Publicly Available Clinical BERT Embeddings
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Probing Biomedical Embeddings from Language Models
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SciBERT: Pretrained Contextualized Embeddings for Scientific Text
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