Enhancing Multi-Label Text Classification by Incorporating Label Dependency to Handle Imbalanced Data

Multi-label text classification (MLTC) holds significant importance in the field of data management and information retrieval, where the distribution of label samples often exhibits a long-tail pattern. This poses a formidable challenge for effectively summarizing test data, particularly for classes with limited sample representation. Existing approaches aimed at addressing this challenge often involve altering the original data distribution, resulting in reduced generalization performance on real-world data and inadequate mitigation of the long-tail problem in samples. To tackle this challenge, we propose a novel method for multi-label text classification called MLTC-LD (an enhanced Multi-Label Text Classification model by incorporating Label Dependency). The primary objective of MLTC-LD is to enhance the performance of classes with scarce data by leveraging knowledge acquired from classes with abundant data. MLTC-LD leverages Graph Attention Networks (GAT) to incorporate prior information associated with labels. It calculates a relationship matrix between label samples and integrates this information into the classifier. We conducted a comprehensive evaluation of MLTC-LD on three datasets and compared its performance against eight baseline models. The experimental results validate the superiority of MLTC-LD in effectively mitigating the challenges posed by long-tail distributions.