计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 237-242.doi: 10.11896/jsjkx.200100036
周晓进1, 徐陈铭2, 阮彤1
ZHOU Xiao-jin1, XU Chen-ming2, RUAN Tong1
摘要： 在现有的面向中文临床电子病历的命名实体识别任务中,实体标注粒度通常过细或过粗,过细的标注结果难以找到实际应用场景,而过粗的标注结果通常需要在进行复杂的处理后,才能明确实体的规范形式和语义类型,以便于后续的数据挖掘应用。为简化处理步骤,根据常见的7类粗粒度临床实体的特点,定义了用以解释粗粒度实体的9类细粒度解析实体。同时,针对多粒度实体的特点,提出了基于多任务学习和自注意力机制的多粒度临床实体识别模型,并在真实的医院电子病历库中标注了5 000条包含多粒度实体的文本以验证模型的效果。实验结果表明,该模型优于主流的序列标注模型,在粗、细粒度实体识别任务中,两者的F1值分别达到了92.88和85.48。
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