GCN-based text classification method

Abstract

The invention discloses a GCN-based text classification method. The method comprises the steps of obtaining a text classification corpus set, wherein the corpus set comprises a plurality of samples, and each sample comprises a title and a chapter; preprocessing the corpus set, and dividing the preprocessed corpus set into a training set, a verification set and a test set; processing the chapters through space, and extracting a graph relationship between words; embedding each word into a low-dimensional real value vector space of the matrix according to the graph relationship; constructing a bidirectional LSTM according to the vector representation of the word, and obtaining sentence representation; reconstructing sentence representation based on a self-attention mechanism, inputting the sentence representation into a GCN neural network, and training a semantic classification model; inputting the text word vector of the verification set into the model, and recording and storing model parameters when the effect on the verification set is optimal; and testing the test set based on the optimal model obtained by the verification set to obtain a classification result. According to the method, a more accurate class result is finally obtained by applying LSTM and GCN in combination with an attention mechanism.

Description

technical field [0001] The present invention relates to the technical field of text classification, in particular to a text classification method based on GCN (Graph convolutional networks, graph convolutional neural network). Background technique [0002] In the past few years, with the rapid development of science and technology, especially the rapid development of the Internet and social networks, all kinds of information are flooding the Internet. Among them, the CSDN blog is developing rapidly, providing a platform for Internet technicians to develop and communicate. Experience, and share the solutions to the problems you encounter, etc.; for everyone to exchange and learn, and also to make a record of your own growth. With the development of the platform, the number of users has increased, and more and more articles have been published. People can obtain a large amount of information through the platform, but how to find the law from these information, discover curren...

AI Technical Summary

Problems solved by technology

[0004] The current text classification models have their own problems. For example, the fastText model does not consider the word order information in the network structure, and the TextCNN model cannot model longer sequence information when the word order information is considered.

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