Automatic Annotation of Mathematical Exercise Topics Based on Subject Knowledge
Annotation of mathematical exercise topics is an essential task for building a structured exercise bank or realizing personalized learning.Due to the particularity of mathematical exercise texts,existing annotation models cannot capture deep key information well,and there are generally problems such as insufficient key knowledge intro-duced,overly direct fusion methods,and a lack of effective screening of information.This paper proposes a model MKAGated for automatic annotation of mathematical exercise topics.The model first uses the pre-trained model to re-present the original exercise and two kinds of refined subject knowledge texts.Then,the attention mechanism is a-dopted to capture the interaction between the exercise and the two subject knowledge texts as the deep representa-tions.Finally,a gated mechanism is applied to implicitly fuse the average pooling of the two deep representations to preserve the actual effective semantic features in the original exercise representation.Experimented on the self-built junior middle school mathematics exercise dataset,the proposed method outperformed the baseline by 1.99%,2.99%and 2.12%according to micro-F1,macro-F1 and weighted-F1,respectively.
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