Chinese User Service Intention Classification Based on Hybrid Neural Network

TL;DR

This paper proposes a hybrid neural network model combining BiLSTM and CNN to improve user service intention recognition accuracy in intelligent systems, effectively handling noisy user descriptions.

Contribution

The study introduces a novel hybrid neural network model that fuses temporal and spatial semantics for better user intention classification.

Findings

Achieved an F1 score of 0.94, outperforming other models.

Effectively handles noise in user requirement descriptions.

Enhances precision in intelligent service systems.

Abstract

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.