融合多特征和迭代扩张卷积的中文电子病历命名实体识别.

Aiming at the problems of word segmentation errors, fuzzy word boundaries and low model calculation efficiency in the process of entity recognition task, a Chinese electronic medical record named entity recognition method that combines multiple features and IDCNN is proposed. This method first constructs a CNN-based character embedding algorithm to train the char vector, then splices it with the word vector and other additional features, then sends it to the iterative expanded convolutional neural network for feature learning, and finally decodes the optimal label sequence through CRF. Experimental results show that the F1 value of this method in the CCKS 2017 Chinese electronic medical record dataset reaches 91.36%, and the training efficiency is better than the existing model, which verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]