EMBERT: A Pre-trained Language Model for Chinese Medical Text Mining

Medical text mining aims to learn models to extract useful information from medical sources. A major challenge is obtaining large-scale labeled data in the medical domain for model training, which is highly expensive. Recent studies show that leveraging massive unlabeled corpora for pre-training language models alleviates this problem by self-supervised learning. In this paper, we propose EMBERT, an entity-level knowledge-enhanced pre-trained language model, which leverages several distinct self-supervised tasks for Chinese medical text mining. EMBERT captures fine-grained semantic relations among medical terms by three self-supervised tasks, including i) context-entity consistency prediction (whether entities are of equivalence in meanings given certain contexts), ii) entity segmentation (segmenting entities into fine-grained semantic parts) and iii) bidirectional entity masking (predicting the atomic or adjective terms of long entities). The experimental results demonstrate that our model achieves significant improvements over five strong baselines on six public Chinese medical text mining datasets.