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Biomedical text mining is becoming increasingly important as the number ofbiomedical documents and web data rapidly grows. Recently, word representationmodels such as BERT has gained popularity among researchers. However, it isdifficult to estimate their performance on datasets containing biomedical textsas the word distributions of general and biomedical corpora are quitedifferent. Moreover, the medical domain has long-tail concepts andterminologies that are difficult to be learned via language models. For theChinese biomedical text, it is more difficult due to its complex structure andthe variety of phrase combinations. In this paper, we investigate how therecently introduced pre-trained language model BERT can be adapted for Chinesebiomedical corpora and propose a novel conceptualized representation learningapproach. We also release a new Chinese Biomedical Language UnderstandingEvaluation benchmark (\textbf{ChineseBLUE}). We examine the effectiveness ofChinese pre-trained models: BERT, BERT-wwm, RoBERTa, and our approach.Experimental results on the benchmark show that our approach could bringsignificant gain. We release the pre-trained model on GitHub:https://github.com/alibaba-research/ChineseBLUE.

Conceptualized Representation Learning for Chinese Biomedical Text Mining