【摘要】 关系抽取是自然语言处理中的核心任务，也是构建医疗领域知识图谱中的关键问题。现有的关系抽取方法鲜有融合实体类别的特征，针对医疗领域中实体类别的特点，提出一种融合实体类别特征的医疗领域关系抽取方法CBBS(category BERT BiLSTM Sigmoid)。首先融入实体类别特征，采用基于Transformer的双向编码器表示(bidirectional ecoder representations from Transformers, BERT)-双向长短时记忆(bidirectional long short-term memory, BiLSTM)-Sigmoid模型进行医疗关系抽取，将医疗领域关系抽取问题转化为序列标注问题，提升了单条语料中同一实体处于多种关系时的抽取效果。对比实验表明，CBBS方法在构建的医疗领域关系抽取数据集上与其他方法相比取得了最好的实验效果，精确率达到了83.97%,能够有效地解决医疗领域关系抽取问题。更多还原

【Abstract】 Relation extraction is the core task in natural language processing, and also a key problem in building a knowledge map in the medical field.The existing relation extraction methods rarely integrate the characteristics of entity categories.In view of the characteristics of entity categories in the medical field, a relation extraction method named CBBS(category BERT BiLSTM Sigmoid)in the medical field integrating entity category features was proposed.The CBBS method incorporates entity category features, uses the BERT(bidirectional ecoder representations from Transformers)-BiLSTM(bidirectional long short-term memory)-Sigmoid model for medical relation extraction, and converts the relation extraction problem in the medical field into a sequence labeling problem, which improves the extraction effect when the same entity in a single corpus is in multiple relationships.The comparative experimental analysis shows that the CBBS method has achieved the best experimental results compared with other methods on the constructed medical field relation extraction dataset, with an accuracy rate of 83.97%,which can effectively solve the relation extraction problem in the medical field.更多还原