Extreme Multi-Label Text Classification Based on Balance Function

Extreme multi-label text classification is a challenging task in the field of natural language processing. In this task, there is a long-tailed distribution situation of labeled data. In this situation, model has a poor ability to learn tail labels classification, which results the overall classification effect is not good. In order to address the above problems, an extreme multi-label text classification method based on balance function is proposed. Firstly, the BERT pre-training model is used for word embedding. Further, the concatenated output of the multi-layer encoder in the pre-trained model is used as the text vector representation to obtain richer text semantic information and improves the model convergence speed. Finally, the balance function is used to assign different attenuation weights to the training losses of different prediction labels, which improves the learning ability of the method on tail label classification. The experimental results on Eurlex-4K and Wiki10- 31K datasets show that the evaluation indicators P@1, P@3 and P@5 respectively reach 86.95%, 74.12%, 61.43% and 88.57%, 77.46% and 67.90%. © The Author(s) 2024.