Text Entity Recognition Model of BiLSTM-CRF Hydraulic Engineering Inspection Based on Word Vector
Named entity recognition is the core technology for constructing water resources knowledge graphs.Hydraulic en-gineering inspection text is the most common data type of hydraulic engineering.Recorded in text form,there is no fixed format and structure,but it contains potential risk information of water conservancy project safety,characterized by high value density.In view of the problem of recognizing named entities in the text of water conservancy project inspection,the BiLSTM-CRF model for word-vector fusion is proposed.Firstly,the inspection text is vectorized in word dimension and word dimension respectively,and word vector is combined to obtain word vector features.Secondly,BiLSTM neural net-work is applied to obtain the serialized contextual features.Finally,it is decoded by CRF and the corresponding entities are extracted.Taking the inspection text of the middle route of South-to-North Water Transfer project as an example,the exper-imental results show that the method combined with word vector can effectively improve the recognition performance.The recognition effect of the entity boundary works better,and the model accuracy,recall and F1 value can reach 93.79%,93.06%and 93.42%,respectively.The time efficiency is 82.86%better than that of the BERT-BiLSTM-CRF model.The BiLSTM-CRF model based on word vector can provide technical support for the rapid construction of hydraulic engineering knowledge graph.
inspection textentity recognitionBiLSTM neural networkWord2Vecconditional vector field
国家科技期刊平台
NSTL
万方数据
