Tagging knowledge concepts for math problems based on multi-label text classification

Tagging knowledge concepts for course problems is essential for intelligent tutoring systems. Traditional manual tagging methods, usually performed by domain experts, are time-consuming and subject to individual biases. Consequently, research on automatic tagging technology is of substantial practical importance. Recently, text classification techniques have been applied to this task; however, these methods are inadequate for math problems due to their complexity, which includes formulaic content and hierarchical relationships among knowledge concepts. Although large language models (LLMs) have also been explored for this purpose, their generative nature and high computational cost pose challenges for direct application in tutoring systems. In this paper, we propose an automatic knowledge concept tagging model LHABS based on RoBERTa. This model integrates hierarchical label-semantic attention, which captures hierarchical knowledge concepts information, and multi-label smoothing, which combines textual features to help reduce overfitting, thus enhancing text classification performance. Our experimental evaluation on four datasets demonstrates that our model outperforms state-of-the-art methods. We also validate the effectiveness of hierarchical label-semantic attention and multi-label smoothing through our experiments. The code and data are available at: https://github.com/xuqiang124/atmk_system.