SWVBiL-CRF: Selectable Word Vectors-based BiLSTM-CRF Power Defect Text Named Entity Recognition

The construction of intelligent informatization of the power grid has prompted to accumulate a large amount of text data, and deeply mining the valuable information is significant for the development of the industry. A large number of basic information and information of production process are recorded in the defect text of power equipment. However, repeated expression, unclear logical expression and colloquialism will appear in the process of recording, which makes the operation and maintenance personnel unable to accurately and efficiently manage the logical relationship between the contents of the text. In this paper, we propose a named entity recognition model of BiLSTM-CRF power defect text based on selectable word vector. The model can recognize the category information of professional named entities in the power field from a large number of power defect texts, thereby structuring the massive power defect text data and facilitating the management of text data. In order to further improve the recognition accuracy of the model, we propose an effective optimization scheme. Simulation experiment results show that the proposed model can accurately recognize the information of named entity of defective text in power field. This research can be seen as the foundation for the future defect analysis of power equipment, auxiliary decision support and so on.