# An ELECTRA-Based Model for Power Safety Named Entity Recognition
## 1 Introduction  
Named Entity Recognition (NER) is crucial for power safety. By identifying key entities in text, such as equipment, components, and operations, it significantly enhances the safety and reliability of power systems. This technology enables the extraction of relevant information from a large number of unstructured text. It improves the accuracy of fault diagnosis, the effectiveness of risk assessment, and the optimization of preventive maintenance. By classifying entity types, NER helps technicians better understand the content of the text. For example, it can distinguish between different types of switchgear, which is crucial for determining the cause of faults, assessing potential risks, and planning maintenance schedules. NER is also extremely important for constructing knowledge graphs [1] from power safety texts. It identifies key information, creates structured data, and builds high-quality datasets. Researching NER in power safety texts is critically important [2]. NER is a subtash of natural language processing that identifies and categorizes entities with specific meanings, such as names, places, organizations, times, and numbers, into predefined categories [3]. NER in power safety texts is highly challenging due to the extensive use of specialized terminology and frequent ambiguities. In the power sector, polysemy is common; for instance, the term \"switch\" could refer to a circuit breaker, an isolator, or other switching devices. Its exact meaning depends on the specific context and deep industry knowledge. Additionally, the hierarchical and subordinate relationships between power entities are complex. For example, in the phrase \"the high-voltage side circuit breaker of the No. 1 main transformer at a substation\", identifying the \"high-voltage side circuit breaker\" as part of the \"No. 1 main transformer\" rather than as an independent device demands high precision in entity extraction. Power systems consist of numerouscomponents such as generators, transformers, lines, and circuit breakers. Their hierarchical relationships must be correctly identified to distinguish whether an entity is a component of another device or an independent device [4]. Traditional NER methods mainly relied on dictionary and rule-based pattern matching and statistical learning approaches, but these often depended on building knowledge bases and dictionaries, requiring significant time and effort. In recent times, there has been significant progress in NER methods based on deep learning. Bidirectional long short-term memory (BiLSTM) [5] is a sequence processing model composed of two Long Short-Term Memory (LSTM) networks. Conditional random field (CRF) [6] is a discriminative probabilistic model that predicts the probability distribution of an output sequence given an input sequence. Some researchers combine BiLSTM with CRF to form a BiLSTM-CRF model [5], which is used for NER tasks. [PERSON] et al. [7] introduced architectures based on BiLSTM and CRF, as well as transition-based methods for NER. With the introduction of the bidirectional encoder representations from the transformers (BERT) model [8] and its derivatives like the robustly optimized BERT (RoBERT) [9], a bile BERT [10], enhanced representation through knowledge integration [11] and efficiently learning an encoder that classifies token replacements accurately (ELECTRA) [12], self-attention-based NER models have become the mainstream approach. Many researchers combine BERT, BiLSTM, and CRF for NER tasks [13]. [PERSON] et al. [14] explores a software entity recognition method based on BERT embeddings. [PERSON] et al. [15] introduce a knowledge graph-inspired NER method, which improved the performance of BERT by integrating knowledge graphs. [PERSON] et al. [16] introduced a BERT-Star-Transformer-CNN-BiLSTM-CRF model, which improves the efficiency of the traditional Transformer by integrating BERT-generated vectors with a Star-Transformer. [PERSON] et al. [17] introduced an NER model for the maintenance records of primary power equipment.