Research on text classification algorithm based on hybrid neural network

Zhiyuan Qiu; Donghong Qin; Chen Xu

doi:10.1117/12.2640881

2 December 2022 Research on text classification algorithm based on hybrid neural network

Zhiyuan Qiu, Donghong Qin, Chen Xu

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881A (2022) https://doi.org/10.1117/12.2640881
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In text classification tasks, using traditional char granularity or word granularity information to encode context information into the same parameter space cannot effectively extract enough text feature information, and the expressive ability of text semantic information is also relatively weak. In order to solve this problem, this paper adopts the embedding method of fusing graphemes and morphemes, and establishes a text classification model that combines a hybrid self-attention mechanism and RCNN. The model first divides the text into char sequences and word sequences and fuses them, and then uses the Transformer encoder to extract preliminary features. The self-attention mechanism in the Transformer can better focus on semantic and location information, and then uses RCNN to extract the deep features which fusion grapheme and morpheme information, the feature vector is calculated and output by the softmax function, and finally the classification of Chinese short texts is realized. The experimental results show that the method proposed in this paper has different degrees of improvement in the accuracy of text classification compared with a single embedding method and a single neural network.

Citation Download Citation

Zhiyuan Qiu, Donghong Qin, and Chen Xu "Research on text classification algorithm based on hybrid neural network", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881A (2 December 2022); https://doi.org/10.1117/12.2640881

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available