Chinese text classification method based on super-deep convolution neural network structure model

Abstract

The invention provides a Chinese text classification method based on a super-deep convolution neural network structure model. The method comprises the steps of collecting a training corpus of a word vector from the internet, combining a Chinese word segmentation algorithm to conduct word segmentation on the training corpus, and obtaining a word vector model; collecting news of multiple Chinese news websites from the internet, and marking the category of the news as a corpus set for text classification, wherein the corpus set is divided into a training set corpus and a test set corpus; conducting word segmentation on the training set corpus and the test set corpus respectively, and then obtaining the word vectors corresponding to the training set corpus and the test set corpus respectively by utilizing the word vector model; establishing the super-deep convolution neural network structure model; inputting the word vector corresponding to the training set corpus into the super-deep convolution neural network structure model, and conducting training and obtaining a text classification model; inputting the Chinese text which needs to be sorted into the word vector model, obtaining the word vector of the Chinese text which needs to be classified, and then inputting the word vector into the text classification model to complete the Chinese text classification.

Description

technical field [0001] The invention relates to the technical fields of natural language processing and deep learning, in particular to a Chinese text classification method based on an ultra-deep convolutional neural network structure model. Background technique [0002] With the explosive growth of network platforms such as mobile Internet, social networking and new media, the network is filled with a large number of texts that lack effective information organization but have research value, and text classification, as one of the key technologies of natural language processing, can effectively solve the problem of information It is widely used in tasks such as search engines, spam filtering, personalized news and data sorting. Therefore, text classification plays an important role in natural language processing, intelligent organization and management of data and other fields. [0003] Traditional text classification mainly relies on knowledge engineering taxonomy. First, ...

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Problems solved by technology

[0006] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a Chinese text classification method based on an ultra-deep convolutional neural network structure (VDCNN for short) model, which solves the problem of excessive vector dimension in Chinese text classification. It is difficult to determine the size of the convolution kernel, the gradient disappears, and the accuracy rate is insufficient in the traditional convolutional neural network.

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