Research on multi-label long text classification algorithm based on transformer-LDA

Abstract

Text classification is an important research area in the field of natural language processing. In view of the low efficiency of the traditional CNN and RNN algorithms for multi-label classification of long text due to timing and spatial displacement problems, this paper proposes a long text classification model with improved Transformer attention mechanism, and combines LDA topic classification algorithm to achieve multi-label classification of document length text. Firstly, the paper introduces the industry's solutions to the problem of multi-classification of long texts. Compared with traditional algorithms such as truncation method and pooling method, the PAPER proposes the LDA topic classification model combined with the improved Transformer-XL text classification model to extract text features with fine granularity, so as to classify texts with higher accuracy. Finally, comparative experiments show that the proposed solution has a significant improvement in P value, R value and F1 value compared with the traditional classification method in the field of long text multi-label classification.