Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
Kaihong Zheng, Lingyun Sun, Xin Wang, Shangli Zhou, Hanbin Li, Sheng Li, Lukun Zeng, Qihang Gong · IEEE Access · 2021
Named Entity Recognition(NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the local context relationship. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.