结合门控机制的卷积网络实体缺失检测方法

Convolutional Network Entity Missing Detection Method Combined with Gated Mechanism

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。

The adequacy of the entity information directly affects the applications that depend on textual entity information,while conventional entity recognition models can only identify the existing entities.The task of the entity missing detection,defined as a sequence labeling task,aims at finding the location where the entity is missing.In order to construct training dataset,three corres-ponding methods are proposed.We introduce an entity missing detection method combining the convolutional neural network with the gated mechanism and the pre-trained language model.Experiments show that the F1 scores of this model are 80.45%for the PER entity,83.02%for the ORG entity,and 86.75%for the LOC entity.The model performance exceeds the other LSTM-based named entity recognition model.It is found that there is a correlation between the accuracy of the model and the word frequency of the annotated characters.