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Text classification method and system based on graph convolutional neural network

A convolutional neural network and text classification technology, applied in the field of graph data mining and graph classification, can solve problems such as difficult to guarantee the overall effect classification model, and achieve the effect of avoiding model overfitting, solid theoretical foundation, and reducing the number of parameters

Inactive Publication Date: 2020-03-27
INST OF INFORMATION ENG CAS
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Problems solved by technology

Although these methods have their own advantages, it is difficult to guarantee the classification model with the best overall effect.

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  • Text classification method and system based on graph convolutional neural network
  • Text classification method and system based on graph convolutional neural network
  • Text classification method and system based on graph convolutional neural network

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[0039] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0040] Such as figure 1 As shown, the text classification algorithm mainly includes five key processes: data preprocessing, building graph structure, graph structure preprocessing, building and training graph convolutional neural network model, and using graph convolutional neural network model to predict text categories. In the following, the specific implementation of the algorithm will be illustrated by respectively elaborating the above five key processes in detail.

[0041] Process 1: Data Preprocessing

[0042] In real data, there are often a lot of redundant information, default values ​​and noise, and the existence of outliers may also be caused by human errors. In addition, as far as the data set used in the proposal of this application i...

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Abstract

The invention discloses a text classification method and system based on a graph convolutional neural network. The method comprises the following steps: 1) for each classified annotated text in a texttraining set in a target field, generating a text feature vector of the text according to the word frequency and inverse document rate of words in the text; combining the text feature vectors to generate a text feature matrix, namely a TF-IDF matrix, and constructing a graph structure of the text training set according to the word vector similarity of the words; 2) training a graph convolutionalneural network by using the graph structure and the text feature matrix; and 3) for a to-be-classified text a in the target field, inputting the text feature vector of the text a into the trained graph convolutional neural network to obtain the category of the text a. According to the method, the semantic structure information of the text is considered, the hidden features of the text are capturedfrom another perspective, and the classification accuracy is high.

Description

technical field [0001] The invention belongs to the field of graph data mining and graph classification, and in particular relates to a text classification method and system based on a graph convolutional neural network. Background technique [0002] With the advent of big data, the scale of data has shown an explosive growth trend, and the relationship between massive heterogeneous data has gradually become closer. Graph is a kind of abstract data structure commonly used to represent the relationship between things. There are closely related data elements in real life, such as social networks and academic networks, which can be represented by graph data. Practical problems can be transformed into technical problems of graphs and data mining. For example, the social software WeChat uses WeChat IDs as nodes, and the relationship between WeChat IDs such as "friendship" and "likes and comments" as the edges of the graph, thereby constructing graph structure data. Its practic...

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Application Information

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IPC IPC(8): G06F16/35G06N3/04
CPCG06F16/35G06N3/045
Inventor 唐钰葆于静曹聪刘燕兵谭建龙郭莉
Owner INST OF INFORMATION ENG CAS
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