华北水利水电大学学报（自然科学版）2024，Vol.45Issue(3)：9-17,9.DOI:10.19760/j.ncwu.zk.2024024
基于字词向量的BiLSTM-CRF水利工程巡检文本实体识别模型
Text Entity Recognition Model of BiLSTM-CRF Hydraulic Engineering Inspection Based on Word Vector
摘要
Abstract
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.关键词
巡检文本/实体识别/双向长短期记忆神经网络/Word2Vec/条件向量场Key words
inspection text/entity recognition/BiLSTM neural network/Word2Vec/conditional vector field分类
信息技术与安全科学引用本文复制引用
刘雪梅,程彭圣男,李海瑞,曹闯,高英,崔培..基于字词向量的BiLSTM-CRF水利工程巡检文本实体识别模型[J].华北水利水电大学学报（自然科学版）,2024,45(3):9-17,9.基金项目
国家自然科学基金项目(72271091) （72271091）
河南省科学院科技开放合作项目(220901008). （220901008）
