Chinese User Service Intention Classification Based on Hybrid Neural Network
This work addresses the challenge of understanding noisy user descriptions for personalized service in Chinese contexts, but it is incremental as it builds on existing neural network techniques.
The paper tackled the problem of user service intention recognition in intelligent service systems by proposing a hybrid neural network model combining BiLSTM and CNN, which achieved an F1 score of 0.94.
In order to satisfy the consumers' increasing personalized service demand, the Intelligent service has arisen. User service intention recognition is an important challenge for intelligent service system to provide precise service. It is difficult for the intelligent system to understand the semantics of user demand which leads to poor recognition effect, because of the noise in user requirement descriptions. Therefore, a hybrid neural network classification model based on BiLSTM and CNN is proposed to recognize users service intentions. The model can fuse the temporal semantics and spatial semantics of the user descriptions. The experimental results show that our model achieves a better effect compared with other models, reaching 0.94 on the F1 score.