  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.
