【摘要】 针对中文电子病历中复杂医疗实体的识别问题，提出一种联合特征与多头注意力相结合的实体识别方法。该方法使用字符、词性和词典组成的联合特征，利用BiLSTM和多头注意力分别提取句子的全局特征和局部特征，利用CRF结合所有特征完成实体标签的预测。实验结果表明，该方法F1值达89.16%,其中治疗和疾病两类实体分别达到94.76%和95.56%。更多还原

【Abstract】 Aimed at the recognition problem of complex medical entities in Chinese electronic medical records(EMRs), an entity recognition method combining joint features and multi-head attention is proposed. This method used the joint feature composed of characters, parts of speech and dictionary, and used BiLSTM and multi-head attention to extract separately the global feature and local feature of the sentence. CRF was used to combine all the features to complete the prediction of the entity labels. Experimental results show that the F1-score of this method reaches 89.16%, among which the two types of entities, treatment and disease, reach 94.76% and 95.56% respectively.更多还原