Chinese short text classification method based on graph attention network

Abstract

The invention discloses a Chinese short text classification method based on a graph attention network, and the method comprises the following steps: preprocessing text data, and obtaining a word listset corresponding to a text; text feature extraction: carrying out word embedding processing on the word list set corresponding to the text by adopting a feature embedding tool to obtain a corresponding word vector; carrying out mapping by adopting a graph structure, and establishing a heterogeneous graph by taking the text and words in the text as graph nodes; establishing a graph attention network text classification model; adopting a Chinese short text data set with category annotation of a network open source as a training language data set, and adopting a heterogeneous graph training graph attention network text classification model; outputting the category to which the text belongs; processing the node features through a softmax classification layer to obtain a final classification category; according to the invention, the text features can be fully extracted under the condition that the short text information amount is insufficient, information with high value for text classification is focused on, and the classification accuracy is effectively improved.

Description

technical field [0001] The invention relates to the research field of computer natural language processing, in particular to a Chinese short text classification method based on a graph attention network. Background technique [0002] In recent years, with the rapid development of computer technology, the Internet and its affiliated industries, countless text-based data are generated on the Internet every day, showing the characteristics of big data. How to quickly classify and analyze massive messy texts is an urgent problem to be solved. Text classification is an important task in natural language processing tasks. It organizes and classifies text resources. At the same time, it is also a key link to solve the problem of text information overload. It is widely used in digital libraries, information retrieval and other fields. Using the correct text classification technology to extract the effective semantic information contained in a large amount of text data, and then mini...

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Problems solved by technology

[0005] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and provide a method for classifying Chinese short texts based on graph attention networks. The present invention solves the short text information shortage by using the method of building maps for the classification of Chinese short texts , and use the graph attention mechanism to solve the problem that the existing classification methods do not focus on the features that are of great value to the classification, resulting in more redundant features in the classification, thus overcoming the current Chinese short text classification method limitations

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