Chinese text classification method based on attention mechanism and feature enhancement fusion

Abstract

The present invention provides a Chinese text classification method based on an attention mechanism and feature enhancement fusion, and belongs to the technical field of data mining. The Chinese textclassification method based on the attention mechanism and the feature enhancement fusion is characterized in that: a feature enhancement fusion Chinese text classification model based on the attention mechanism, a long short-term memory (LSTM) network and a convolutional neural network (CNN), and a feature difference enhanced attentional algorithm model are proposed; and according to the featureenhancement fusion Chinese text classification model, double-layer LSTM and CNN modules are used to sequentially perform enhancement fusion on the text features extracted by using the attention mechanism, the richness of the extracted text features can be continuously enhanced, and the contained text features can be more comprehensive and detailed, so that the recognition ability of the model to the Chinese text features is improved.

Description

technical field [0001] The invention relates to a Chinese text classification method based on attention mechanism and feature strengthening fusion, which belongs to the technical field of data mining. Background technique [0002] With the popularization of Internet applications, the number of electronic documents on the Internet is growing rapidly; in order to quickly, accurately and comprehensively mine effective information from massive electronic documents, text classification technology has received widespread attention in recent years; Chinese texts are becoming more and more abundant, and the utilization rate of Chinese information is also increasing. Therefore, it is of great practical significance to automatically classify Chinese texts. [0003] In the field of natural language processing, through the design and improvement of artificial intelligence-related algorithm models based on neural networks, the proposed algorithm model is more in line with the style chara...

AI Technical Summary

Problems solved by technology

Therefore, the character-level question answering system based on the attention mechanism has better performance; but its processing granularity is only at the character level, and if the attention generated by the text at the word-level and sentence-level granularity can be considered comprehensively, the features will be more abundant.

[0004] In the case of relatively long Chinese text, there will be problems in text classification, and the text components containing important text feature information in the text are unevenly distributed in the text; the attention mechanism can reflect the different texts in the Chinese text. The size of the role played by elements in the text recognition process assigns greater weights to important text elements, but the learned weight matrix is ​​obtained based on iterative training of the neural network, which is a process of continuous learning of the neural network. There is no guarantee that all the weights are properly configured, which may lead to insufficient feature extraction or the deep semantics of the Chinese text contained in the extracted features are not comprehensive enough

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