Text classification method and device

Abstract

The invention discloses a text classification method and device. The method comprises the following steps of: receiving a plurality of training texts, the categories of which are known; constructing graph structures of the training texts by adoption of a collinear relationship of words after preprocessing the training texts; training parameters of a convolutional neural network through a counter-propagation algorithm according to the graph structures of the training texts, so as to obtain a trained convolutional neural network; receiving an input to-be-classified text; constructing a graph structure of the to-be-classified text by adoption of the collinear relationship of the words after preprocessing the to-be-classified text; and predicting a category technical scheme of the to-be-classified text through the trained convolutional neural network according to the graph structure of the to-be-classified text. According to the method and device, the convolutional neural network is used for carrying out text classification, so that the text classification correctness and credibility are improved.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a text classification method and device. Background technique [0002] Convolutional neural network is an artificial neural network with deep learning ability designed according to the principle of primate visual neural mechanism. Hubel and Wiesel proposed a visual structure model based on cat visual cortex in 1962, and proposed the concept of receptive field for the first time. However, with the emergence of simpler and more efficient linear classifiers such as support vector machines, and due to the local minimum limitations commonly found in non-convex objective cost functions of deep structures, the research on neural networks has fallen into a low ebb for nearly two decades. It is known that Hinton et al. proposed an unsupervised bead layer training method based on the deep belief network (DBN), which solved the optimization problem related to the deep structure. ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiments of the present invention is to provide a text classification method and device, which can effectively solve the problem of lack of application of convolutional neural network for text classification in the prior art, and improve the accuracy and credibility of text classification

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