Automatic composition classification method for primary school based on TextRank and convolution neural network

Abstract

The invention belongs to the field of educational informatization, an automatic composition classification method for primary school based on TextRank and convolution neural network is provided, firstly, the TextRank-based key sentence extraction model is used to extract key sentences for various compositions to remove redundant semantic information, and then the convolution neural network is usedto extract fixed-length text feature vectors for training classifiers and predicting text categories. The method of the invention uses TextRank algorithm to eliminate redundant information of data set in advance, and reduces interference information of long text compared with other depth learning methods; the feature selection of the method of the invention is automatically completed, and the efficiency is improved compared with the traditional machine learning method.

Description

technical field [0001] The invention belongs to the field of educational informatization, and relates to an automatic classification method for primary school composition based on TextRank and a convolutional neural network. Background technique [0002] As we all know, reading model essays is an important method for students to learn writing, and reading model essays can significantly improve primary school students' writing performance. Therefore, the rapid construction of model essay material databases is an important link to realize the auxiliary means of writing information. [0003] Text classification is a classic topic in the field of natural language processing. It refers to determining a category for each document in a document collection according to a predefined subject category. With the advent of the data age, the number of electronic documents on the Internet has increased significantly. Text classification has become a key technology in information retrieval ...

AI Technical Summary

Problems solved by technology

[0006] At present, most of the research on Chinese text classification is about the binary classification of sentiment classification or the classification of short texts with relatively simple semantics such as news and Weibo. Their general method is to directly use the data set to train the classifier. The data set is pre-processed. Compared with news and Weibo, there are more categories of primary school compositions, longer texts and richer semantic information. If the existing methods are used for composition classification tasks, the effect will not be ideal.

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