Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion

Convolutional neural networks (CNNs) face challenges in capturing long-distance text correlations, and Bidirectional Long Short-Term Memory (BiLSTM) networks exhibit limited feature extraction capabilities for text classification of public service requests. To address the abovementioned problems, this work utilizes an ensemble learning approach to integrate model elements efficiently. This study presents a method for classifying public service request text using a hybrid neural network model called BERT-BiLSTM-CNN. First, BERT (Bidirectional Encoder Representations from Transformers) is used for preprocessing to obtain text vector representations. Then, context and process sequence information are captured through BiLSTM. Next, local features in the text are captured through CNN. Finally, classification results are obtained through Softmax. Through comparative analysis, the method of fusing these three models is superior to other hybrid neural network model architectures in multiple classification tasks. It has a significant effect on public service request text classification.