Research on Named Entity Recognition in Fault Text of Railway Signal Equipment
Hao Su, Shiwu Yang, Chang Liu, Haiwei Liu · 2022
The unstructured text of railway signal equipment failure records important information such as the failure cause and failure phenomenon of the signal equipment. Most of them are stored in Word, Excel, etc. The traditional technology cannot explore the important value contained in the text data. In order to convert the analysis of the fault causes of the signal equipment recorded in the text into knowledge that can serve fault diagnosis, this paper uses the BiLSTM+CRF model to realize named entity recognition and analyzes 638 fault texts of railway signal equipment in a railway field from 2021 to 2022. The accuracy of the model reaches 83.38%, which shows that the named entity recognition model of railway signal fault equipment has a high evaluation standard and can be applied to the extraction of signal equipment fault entities based on text mining.