Mathematical problem text multi-label classification method based on mathematical feature extraction

A feature extraction and classification method technology, applied in the field of multi-label text classification of mathematics problems, can solve problems such as difficulty in feature learning, reduction in the proportion of sparse data, and high noise, so as to improve mathematics learning performance, improve label classification accuracy, and solve noise The effect of interference

Pending Publication Date: 2022-08-09
JIANGSU UNIV
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Problems solved by technology

Moreover, using the method of machine learning, the model effect is greatly affected by the artificial selection of features.
There is a lot of noise, and the proportion of sparse data decreases, which leads to the disappearance of data aggregation effect and makes feature learning difficult.
Ye et al. adopt

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  • Mathematical problem text multi-label classification method based on mathematical feature extraction
  • Mathematical problem text multi-label classification method based on mathematical feature extraction
  • Mathematical problem text multi-label classification method based on mathematical feature extraction

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Embodiment Construction

[0060] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0061] like figure 1 and 3 The illustrated method for multi-label classification of mathematical text based on mathematical feature extraction includes the following steps:

[0062] Step 1, collect the test questions in multiple sets of mathematics test papers as samples to form a sample set; and use the knowledge points of each question as the label of the sample; more specifically, each sample (ie, test question) corresponding to knowledge points.

[0063] The samples and their labels are preprocessed and feature extracted, and the sample feature vector corresponding to the sample and...

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Abstract

The invention discloses a mathematical problem text multi-label classification method based on mathematical feature extraction, and the method comprises the steps: taking mathematical problem test questions as samples, and taking knowledge points as sample labels; carrying out preprocessing and feature extraction on the sample and the label thereof, and coding a sample feature vector to obtain a hidden layer vector; a self-attention mechanism is cited to calculate the attention weight of each hidden layer vector, and a feature vector output by the text is obtained; dividing the answer analysis text into leaf nodes and root nodes, and forming a feature matrix of a feature priori tree by leaf node text information features and root node text information features; performing mathematical feature extraction on the sample feature vector and the feature matrix of the feature priori tree, inputting the feature vector output by the text and the output result of the mathematical feature extraction part into a classifier, and outputting a classification result by the classifier; a training stop condition is set, and when training stops, a trained mathematical text multi-label classification model is obtained; and utilizing the mathematical text multi-label classification model to effectively classify mathematical problem texts.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a multi-label text classification method for mathematical questions combined with prior knowledge and mathematical feature extraction. Background technique [0002] In recent years, with the development of computing power, the theory and application of artificial intelligence have made breakthroughs, and have been widely implemented in the fields of computer vision, natural language processing, and recommendation algorithms, and have been integrated into all aspects of daily life. For example, ubiquitous biometric recognition technology; recommendation technology integrated into various consumption scenarios; intelligent customer service, machine translation, text mining, risk control, assisted driving and other technologies have greatly changed people's lifestyles. The use of artificial intelligence technology to replace repetitive human labor and improve efficiency ha...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06K9/62G06N3/04G06N3/08
CPCG06F16/35G06N3/08G06N3/047G06N3/044G06F18/214G06F18/24
Inventor 侯骏周从华朱小龙
Owner JIANGSU UNIV
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