Intelligent classification of operation and maintenance issues for real estate registration in Beijing
To improve the efficiency of daily operation and maintenance for real estate registration in Beijing and address the issues of low efficiency and long response time in manual processing,this paper proposed an automatic classification method for operation and maintenance issues based on the bidirectional encoder representations from transformers(BERT).Firstly,the BERT model was utilized to extract contextual semantic features of the operation and maintenance issue texts.Secondly,global max pooling technology was applied to extract the key category features of the texts.Finally,the SoftMax function was used to calculate the probabilities of each category,and the category with the highest probability was selected as the classification result.Experimental results demonstrate that the macro precision(MP),macro recall(MR),and macro-average F1 score of the method proposed in this paper all exceed 93%,significantly surpassing common text classification techniques.This fully proves the effectiveness of the method and provides certain reference significance for constructing an intelligent operation and maintenance system for real estate registration.
real estateintelligent classificationpre-trained language modelbidirectional encoder representation from transformer(BERT)dataset construction
万方数据
维普
