Named entity recognition method in utility boiler domain based on CRF-BiLSTM-BERT model

Utility boilers are important equipment in the thermal power generation industry. Ensuring their safe and stable operation is of great significance to social and economic development. The utility boiler system has complex components and numerous management processes. Massive multimodal data will be generated during operation and inspection of utility boilers. However, for unstructured information, traditional data analysis methods are not applicable, and it is necessary to rely on knowledge graph technology to fully mine and utilize its data value. This paper research on the named entity recognition technology in utility boiler domain, which is the core technology for establishing knowledge graph in utility boiler domain. Since utility boiler is a highly technical professional domain, general datasets cannot meet the requirements of model training. A special dataset and entity labeling system in utility boiler domain is established. At the same time, an improved CRF-BiLSTM-BERT model is proposed, which consists of three-layer structure, to efficiently implement named entity recognition in utility boiler domain. After experimental verification, the accuracy of entity recognition can reach more than 90%. In summary, the named entity extraction model and data-set, which is highly adaptable to utility boilers, established in this paper can meet the data requirements for the subsequent establishment of professional knowledge graph in utility boiler domain.