Text enhancement method, text classification method and related devices

Abstract

The invention provides a text enhancement method, a text classification method and a related device. The method comprises the steps: acquiring a statement content in a text corpus, performing word segmentation on the statement content, and obtaining words obtained after word segmentation; screening out similar words of which the similarity with the segmented words exceeds a threshold value from the concept tree, and randomly replacing the statement content by using the similar words to obtain a plurality of statements; training a generative adversarial network by using a plurality of statements to obtain a generative adversarial network model; generating an expansion statement sample by using a generative adversarial network model; and combining the expanded statement sample with the statement content in the text corpus to obtain an enhanced text data set. In the implementation process, the expanded statement sample is generated by using the generative adversarial network model obtained by training, and the generative adversarial network model learns the newly added change rule between the similar words with the similarity exceeding the threshold value in the concept tree, so a specific category sample can be better generated.

Description

technical field [0001] The present application relates to the technical fields of machine learning, artificial intelligence and deep learning, and specifically relates to a text enhancement method, a text classification method and related devices. Background technique [0002] At present, most of the sentences of the text corpus are used to train the neural network model, and a lot of samples are often needed to train the neural network model to achieve better results. However, when the number of text samples of a specific category (for example: prohibited words and prohibited words) used to train the neural network model is small, the correct rate of the trained neural network model is low. In order to increase the recognition accuracy of samples of a specific category, it is common practice to rely on humans to collect as many text sentence samples as possible, or to manually compose more text sentence samples. However, this approach is not only inefficient but also not e...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the embodiments of the present application is to provide a text enhancement method, a text classification method and related devices, which are used to improve the problem of low efficiency in collecting samples of specific categories

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