Chinese Named Entity Recognition Within the Electric Power Domain

The field of electrical power encompasses a vast array of diverse information modalities, with textual data standing as a pivotal constituent of this domain. In this study, we harness an extensive corpus of textual data drawn from the electrical power systems domain, comprising regulations, reports, and other pertinent materials. Leveraging this corpus, we construct an Electrical Power Systems Corpus and proceed to annotate entities within this text, thereby introducing a novel Named Entity Recognition (NER) dataset tailored specifically for the electrical power domain. We employ an end-to-end deep learning model, the BERT-BiLSTM-CRF model, for named entity recognition on our custom electrical power domain dataset. This NER model integrates the BERT pre-trained model into the traditional BiLSTM-CRF model, enhancing its ability to capture contextual and semantic information within the text. Results demonstrate that the proposed model outperforms both the BiLSTM-CRF model and the BERT-softmax model in NER tasks across the electrical power domain and various other domains. This study contributes to the advancement of NER applications in the electrical power domain and holds significance for furthering the construction of knowledge graphs and databases related to electrical power systems.