Fused attention model-based Chinese text classification method

Abstract

The invention discloses a fused attention model-based Chinese text classification method. The method comprises the following steps of: respectively segmenting a text into a corresponding word set anda corresponding character set through word segmentation preprocessing and character segmentation preprocessing, and training a word vector and a character vector corresponding to the text by adoptionof a feature embedding method according to the obtained word set and character set; respectively carrying out semantic encoding on the word vector and the character vector by taking a bidirectional gate circulation unit neural network as an encoder, and obtaining a word attention vector and a character attention vector in the text by adoption of a word vector attention mechanism and a character vector attention mechanism; obtaining a fused attention vector; and predicting a category of the text through a softmax classifier. The method is capable of solving the problem that more redundant features exist in the classification process as existing Chinese text classification methods neglects character feature information of texts, the extracted texts are single in features, all the pieces of semantic information of the texts are difficult to cover and features having obvious contribution to the classification are not focused.

Description

technical field [0001] The invention belongs to the field of natural language processing, and in particular relates to a method for classifying Chinese texts. Background technique [0002] In recent years, with the rapid development of electronic information technology, massive amounts of information and data in the form of text flood the Internet. How to effectively classify these texts and then mine valuable information has become one of the hotspots in the field of natural language processing research. The purpose of text classification is to assign texts into some predefined topic category. Most of the traditional text classification algorithms are based on shallow machine learning models. Recently, with the great success of deep learning in computer vision and speech recognition, more and more research attempts to apply deep learning to Chinese text classification. Different from the traditional text classification method, the deep learning method learns the word feat...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a Chinese text classification method based on the fusion attention model, which solves the problem that the existing Chinese text classification method ignores the word feature information of the text, resulting in a single extracted text feature, which is difficult to cover all texts. semantic information

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