Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (4): 288-293.doi: 10.11896/jsjkx.211100016

• Artificial Intelligence • Previous Articles     Next Articles

Text Classification Method Based on Word2Vec and AlexNet-2 with Improved AttentionMechanism

ZHONG Gui-feng1, PANG Xiong-wen2, SUI Dong3   

  1. 1 College of Computer Science&Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China;
    2 School of Computer, South China Normal University, Guangzhou 530631, China;
    3 School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102406, China
  • Received:2021-11-01 Revised:2022-01-23 Published:2022-04-01
  • About author:ZHONG Gui-feng,born in 1983,postgraduate,lecturer.Her main research interests include data analysis and mi-ning,machine learning and applications of artificial intelligence.
  • Supported by:
    This work was supported by the National Natural Science Youth Fund(61702026),2020 Guangdong University Scientific Research Project(2020GXJK201),2019 Guangdong University Scientific Research Project(2019KTSCX243) and Special Program for Higher Education in Guangdong Province in 2021(2021GXJK275).

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Abstract: In order to improve the accuracy and efficiency of text classification, a text classification method based on Word2Vec text representation and AlexNet-2 with improved attention mechanism is proposed.Firstly, Word2Vec is adopted to embed the text word features, and the word vector is trained to represent the text in the form of distributed vectors.Then, an improved AlexNet-2 is used to effectively encode the long-distance word dependency.Meanwhile, the attention mechanism is added to the model to learn the contextual embedding semantics of the target word efficiently, and the word weight is adjusted according to the correlation between the input of word vector and the final prediction result.The experiment is evaluated in three public data sets, and the situations of a large number of sample annotations and a small number of sample annotations are analyzed.Experimental results show that, compared with the existing excellent methods, the proposed method can significantly improve the performance and efficiency of text classification.

Key words: AlexNet-2 model, Attention mechanism, Contextual embedding, Text classification, Word dependency

CLC Number: 

  • TP391
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