sis 23(6):

Research Article

Word Embedding for Text Classification: Efficient CNN and Bi-GRU Fusion Multi Attention Mechanism

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  • @ARTICLE{10.4108/eetsis.3992,
        author={Yalamanchili Salini and Poluru Eswaraiah and M. Veera Brahmam and Uddagiri Sirisha},
        title={Word Embedding for Text Classification: Efficient CNN and Bi-GRU Fusion Multi Attention Mechanism},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={9},
        keywords={Text categorization, Deep learning, Convolution neural network, CNN, Gate recurrent unit, GRU, Attention},
        doi={10.4108/eetsis.3992}
    }
    
  • Yalamanchili Salini
    Poluru Eswaraiah
    M. Veera Brahmam
    Uddagiri Sirisha
    Year: 2023
    Word Embedding for Text Classification: Efficient CNN and Bi-GRU Fusion Multi Attention Mechanism
    SIS
    EAI
    DOI: 10.4108/eetsis.3992
Yalamanchili Salini1,*, Poluru Eswaraiah2, M. Veera Brahmam2, Uddagiri Sirisha3
  • 1: V R Siddartha Engineering College
  • 2: Vellore Institute of Technology University
  • 3: P V P Siddhartha Institute of Technology
*Contact email: yalamanchilisalini@gmail.com

Abstract

The proposed methodology for the task of text classification involves the utilization of a deep learning algorithm that integrates the characteristics of a fusion model. The present model is comprised of several attention-based Convolutional Neural Networks (CNNs) and Gate Recurrent Units (GRUs) that are organized in a cyclic neural network. The Efficient CNN and Bi-GRU Fusion Multi Attention Mechanism is a method that integrates convolutional neural networks (CNNs) and bidirectional Gated Recurrent Units (Bi-GRUs) with multi-attention mechanisms in order to enhance the efficacy of word embedding for the purpose of text classification. The proposed design facilitates the extraction of both local and global features of textual feature words and employs an attention mechanism to compute the significance of words in text classification. The fusion model endeavors to enhance the performance of text classification tasks by effectively representing text documents through the combination of CNNs, Bi-GRUs, and multi-attention mechanisms. This approach aims to capture both local and global contextual information, thereby improving the model’s ability to process and analyze textual data. Moreover, the amalgamation of diverse models can potentially augment the precision of text categorization. The study involved conducting experiments on various data sets, including the IMDB film review data set and the THUCNews data set. The results of the study demonstrate that the proposed model exhibits superior performance compared to previous models that relied solely on CNN, LSTM, or fusion models that integrated these architectures. This superiority is evident in terms of accuracy, recall rate, and F1 score.