Few-shot Text Classification Method Based on Feature Optimization

For the poor effect of few-shot text classification caused by insufficient data for feature representation, this paper combines wide and deep attention bidirectional long short time memory (WDAB-LSTM) and a prototypical network to optimize text features for better classification performance. With this proposed algorithm, text enhancement and preprocessing are firstly adopted to solve the problem of insufficient samples and WDAB-LSTM is used to increase word attention to get output vectors containing important context-related information. Then the prototypical network is added to opti-mize the distance measurement module in the model for a better effect on feature extraction and sample representation. To test the performance of this algorithm, Amazon Review Sentiment Classification (ARSC), Text Retrieval Conference (TREC), and Kaggle are selected. Compared with the Siamese network and the prototypical network, the proposed algorithm with feature optimization has a relatively higher accuracy rate, precision rate, recall rate, and F1 value.