Text classification method and device, equipment and storage medium

Abstract

The embodiment of the invention belongs to the field of artificial intelligence, and particularly relates to a text classification method, device and equipment and a storage medium, and the method comprises the following steps: obtaining a to-be-analyzed text, carrying out word segmentation on the text to form a segmented word set, and obtaining subject terms of the text according to a subject model to form a subject term set; obtaining a word embedding vector of each word in the subject word set, performing dimension reduction on the word embedding vectors, mapping the word embedding vectors to a plane, and constructing a Voronoi diagram according to mapping points on the plane; calculating a semantic distance between the non-subject terms and the subject terms, and adding the non-subject terms into a Voronoi diagram; identifying a word node type of each word in the Voronoi diagram, and calculating a semantic distance between word nodes through a corresponding algorithm according to the word node type; and inputting the semantic distance between the word nodes into a pre-constructed graph convolutional neural network to output a graph implicit vector, and carrying out text classification according to the graph implicit vector. According to the invention, the accuracy of text classification is improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a text classification method, device, equipment and storage medium. Background technique [0002] Text classification is a common task in the field of natural language processing. In recent years, from the traditional classification method based on feature engineering + machine learning, such as extracting features based on TF-IDF (word frequency-inverse document frequency) and then training machine learning classifiers, to various methods based on CNN (convolutional neural network), RNN ( Recurrent neural network) deep learning method introduces text classification, such as fasttext, textCNN, TextRNN, etc. Another example is the emergence of pre-trained language models BERT (transformer-based bidirectional encoder representation technology) and ELMO (language model embedding) based on deep neural networks in recent years, which have greatly improved the ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiment of the present invention is to propose a text classification method, device, equipment and storage medium, which solves the problem of text classification based on the capture of local features in the prior art, extracts semantic features from multiple dimensions, and improves the Accuracy of text classification

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