Automatic tagging of knowledge points for K12 math problems

TL;DR

This paper introduces the LABS model, combining label-semantic attention and multi-label smoothing, to improve automatic knowledge point tagging for K12 math problems, addressing the complexity of math texts.

Contribution

It proposes a novel LABS model that integrates label semantics and multi-label smoothing to enhance accuracy in math problem tagging.

Findings

LABS model outperforms traditional BiLSTM in precision, recall, and F1-score.

Label-semantic attention guides neural networks to extract meaningful information.

Multi-label smoothing improves prediction for new data and overall classification accuracy.

Abstract

Automatic tagging of knowledge points for practice problems is the basis for managing question bases and improving the automation and intelligence of education. Therefore, it is of great practical significance to study the automatic tagging technology for practice problems. However, there are few studies on the automatic tagging of knowledge points for math problems. Math texts have more complex structures and semantics compared with general texts because they contain unique elements such as symbols and formulas. Therefore, it is difficult to meet the accuracy requirement of knowledge point prediction by directly applying the text classification techniques in general domains. In this paper, K12 math problems taken as the research object, the LABS model based on label-semantic attention and multi-label smoothing combining textual features is proposed to improve the automatic tagging of knowledge points for math problems. The model combines the text classification techniques in general domains and the unique features of math texts. The results show that the models using label-semantic attention or multi-label smoothing perform better on precision, recall, and F1-score metrics than the traditional BiLSTM model, while the LABS model using both performs best. It can be seen that label information can guide the neural networks to extract meaningful information from the problem text, which improves the text classification performance of the model. Moreover, multi-label smoothing combining textual features can fully explore the relationship between text and labels, improve the model's prediction ability for new data and improve the model's classification accuracy.