Intelligent recognition of named entities of coal mine safety hidden danger based on ERNIE-BiGRU-CRF model
In order to fully explore the key text knowledge of coal mine safety hidden danger and help the safety management personnel of coal mine enterprises to better investigate and manage hidden danger,a named entity recognition method based on pre-training language model was proposed.Firstly,entity categories of coal mine safety hidden danger were defined,and 7 entity categories and 15 entity labels were constructed using BIO labeling strategy.Then,the collected data are preprocessed,and relevant entities were manually marked by experts in the field of coal mine safety,and 1500 standard data sets of named entities of coal mine safety hidden danger were obtained.Finally,the text word vector of coal mine safety hidden danger was represented with ERNIE pre-training model,the context semantic features was extracted with BiGRU structure and the entity labels was decoded with CRF model,thus to complete the named entity recognition of coal mine safety hidden danger.The experimental results show that:the accuracy,recall and F1 value of ERNIE-BiGRU-CRF model on sequence labeling tasks are 56.69%,69.23%and 62.34%,respectively,which are 6.85%,13.74%and 9.83%higher than baseline model of BiLSTM-CRF.And there is little difference between the entity prediction results and the actual label results.In addition,it was verified by the ablation experiment that,BiGRU layer can better capture semantic dependency of text context for coal mine safety hidden danger and CRF layer can further optimize label sequence.
NETL
NSTL
万方数据
