Multi-model Fusion for Text Classification Research of Intelligent Robots
At present,the application of intelligent robot becomes more and more widely,many commercial malls are equipped with intelligent robots to reduce artificial customer service work,it also can save most of the time for customers,for intelli-gent robot received instruction intention recognition inaccurate problem,deep learning model combined with RNN and BILSTM is proposed.First of all,the text is processed,after the embedding layer is converted into word vector,it first passes through the RNN layer,and then the output results of the cyclic neural network are input into the BILSTM.After passing through the linear layer,the obtained results are classified by the Softmax function.Finally,after the verification and comparison of several groups of experi-ments,the experimental results show that compared with the single deep learning model,the recall rate,precision rate and F1-score of the fusion model using RNN-BILSTM are respectively increased by 4.31%,3.64%,3.6%and 1.5%.It is proved that the model fusion is reasonable and effective for the robot's intention recognition text classification task.
model fusiontext classificationintent recognitionRNN-BILSTM
国家科技期刊平台
NSTL
万方数据
