Classifying ultra‐short scientific texts using a hybrid hierarchical multi‐label classification framework

Scientific text classification is essential for efficiently organizing and assimilating scientific knowledge. However, existing methods struggle to classify ultra‐short scientific texts due to their limited content and complex hierarchical labeling. To overcome these challenges, we introduce the BERT‐HMCN framework, which combines Bidirectional Encoder Representations from Transformers (BERT) with a Hierarchical Multi‐label Classification Network (HMCN). This framework introduces a novel level‐fixed fine‐tuning strategy that strengthens the connection between text semantics and hierarchical labels, enhancing the representation of ultra‐short texts. We evaluated BERT‐HMCN's performance on a dataset of 75,065 program titles from the National Natural Science Foundation of China. Our results show that BERT‐HMCN outperforms existing models in both overall performance and hierarchical accuracy. We also conducted a comparative analysis with autoregressive large language models (LLMs), illustrating the strengths of each in different contexts. Further analysis confirms the effectiveness and robustness of the BERT‐HMCN framework. We discuss its theoretical contributions and practical applications, underscoring the broader implications of these results in scientific text classification and other related fields.