计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 262-269.doi: 10.11896/jsjkx.220400126
叶瀚, 李欣, 孙海春
YE Han, LI Xin, SUN Haichun
摘要： 实体信息充足与否直接影响着有赖于文本实体信息的相关应用,而常规的实体识别模型仅能对已存在的实体进行识别。文中提出以序列标注任务定义实体缺失检测任务,并提出了相应的3种实体缺失检测模型的训练数据构造方法。根据实体缺失任务的识别特点,提出了融合门控机制的卷积神经网络与预训练语言模型相结合的实体缺失检测方法。通过实验发现,基于预训练语言模型与门控卷积网络的模型对人名类、组织类、地点类实体缺失识别的F1最高分别达80.45%,83.02%和86.75%,显著高于基于LSTM的实体识别模型。通过字频统计发现,运用不同标注方法的数据集所训练的模型的准确率与被标注字符字频存在相关性。
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