Design and Implementation of Feature Fusion Algorithm for Natural Language Processing Based on BERT

BERT (Bidirectional Encoder Representations from Transformers) model, as a pre-training language model based on transformer architecture, can capture rich contextual information and provide high-quality feature representation for downstream tasks. In this study, three methods are adopted: multi-scale feature fusion, feature fusion guided by attention mechanism and end-to-end feature learning to enhance the model's understanding of text details and structure, realize more flexible feature fusion, and allow the model to learn the optimal feature representation directly from the data. Feature fusion algorithm includes three key parts: feature extraction, feature fusion strategy and fused model structure, in which weighted stitching strategy is used to effectively fuse BERT features and task-related features. In the experimental part, IMDb movie review data set and 20 Newsgroups data set are selected for sentiment analysis and text classification tasks respectively. The results show that compared with BERT model and traditional machine learning model, the proposed feature fusion algorithm has significantly improved in accuracy, F1 score and AUC-ROC. In addition, the ablation experiment further verified the effectiveness of the feature fusion strategy.