Segmentation, fusion, and representation: A novel approach to multi-label classification for long texts

Abstract

Multi-label text classification (MLTC) is a vital task in natural language processing (NLP), often requiring high-quality text representations generated by pre-trained language models (PLMs). However, the inherent input length constraints of PLMs limit their capacity to handle long texts effectively. To address this challenge, we propose an innovative framework for multi-label long text classification. Our approach incorporates a dynamic text segmentation algorithm that optimally partitions long texts, thereby mitigating the input length limitations of PLMs. Additionally, we enhance both text and label representations by integrating external knowledge, modeling label co-occurrence relationships, and employing attention mechanisms. Extensive experiments conducted on diverse MLTC datasets demonstrate the superior performance of our method and uncover intricate relationships between texts and their associated labels. The code is available at https://github.com/Coder-Jeffrey/SKFRL