Efficient Agricultural Question Classification With a BERT-Enhanced DPCNN Model

Guo, Xiaojuan; Wang, Jianping; Gao, Guohong; Zhou, Junming; Li, Yancui; Cheng, Zihao; Miao, Guoyi

doi:10.1109/access.2024.3438848

IEEE Access

2024

DOI: 10.1109/access.2024.3438848

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Efficient Agricultural Question Classification With a BERT-Enhanced DPCNN Model

Xiaojuan Guo,

Jianping Wang,

Guohong Gao

et al.

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2024

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Cited by 2 publications

References 37 publications

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Research on the Application of Improved BERT‐DPCNN Model in Chinese News Text Classification

Wang,

Zhang

2024

Concurrency and Computation

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This paper introduces an enhanced BERT‐DPCNN model for the task of Chinese news text classification. The model addresses the common challenge of balancing accuracy and computational efficiency in existing models, especially when dealing with large‐scale, high‐dimensional text data. To tackle this issue, the paper proposes an improved BERT‐DPCNN model that integrates BERT's pre‐trained language model with DPCNN's efficient convolutional structure to capture deep semantic information and key features from the text. Additionally, the paper incorporates the zebra optimization algorithm (ZOA) to dynamically optimize the model's hyperparameters, overcoming the limitations of manual tuning in traditional models. By automatically optimizing hyperparameters such as batch size, learning rate, and the number of filters through ZOA, the model's classification performance is significantly enhanced. Experimental results demonstrate that the improved ZOA‐BERT‐DPCNN model outperforms traditional methods on the THUCNEWS Chinese news dataset, not only verifying its effectiveness in news text classification tasks but also showcasing its potential to enhance classification performance.

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Research on the Application of Improved BERT‐DPCNN Model in Chinese News Text Classification

Wang,

Zhang

2024

Concurrency and Computation

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Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network

Guo,

Wang,

Gao

et al. 2024

Electronics

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With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems.

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Efficient Agricultural Question Classification With a BERT-Enhanced DPCNN Model

Cited by 2 publications

References 37 publications

Research on the Application of Improved BERT‐DPCNN Model in Chinese News Text Classification

Research on the Application of Improved BERT‐DPCNN Model in Chinese News Text Classification

Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network

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