Simulation of Consumer Complaint Information Extraction Based on Deep Learning
The complaint information of the complaint platform contains a lot of consumers· feedback on product and service problems.It can quickly,completely and accurately identify the types of problems from consumer com-plaint information and extract relevant entities,which can provide effective assistance for businesses to timely improve services and improve user satisfaction.Based on the complaint data of the black Cat complaint platform,this paper u-ses the pipeline method to extract entity-relationship.In this paper,the Bert-BiLSTM model was constructed to ex-tract multi-label types between entities,and the Bert-BiLSTM-CRF model was also established to extract entity pairs.Experimental results show that the entity-multi-label extraction model proposed in this paper has good per-formance,and compared with the benchmark model,the F1-Score evaluation index is improved by 0.96%~3.22%.