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5 July 2024 A text classification model based on BERT and TextCNN
Qi Wu, Bing Xu
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841E (2024) https://doi.org/10.1117/12.3032898
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
As a fundamental task in natural language processing, text classification plays an important role in various applications such as information retrieval, machine translation, and sentiment analysis. However, most deep models do not fully consider the rich information in the training instances during prediction, resulting in incomplete learned text features. To address this problem, this paper proposes the BERT-TextCNN model, which uses BERT to obtain word embeddings with sentence-level global features and uses the embeddings as inputs to TextCNN to capture local features, achieving high accuracy in text classification. To verify the superiority of the model, comparisons with several classic models were conducted, and the test results on the THUCNews dataset showed that the BERT-TextCNN model had improvements in accuracy, recall, and F1 score. Therefore, this model provides an effective solution for text classification tasks.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Qi Wu and Bing Xu "A text classification model based on BERT and TextCNN", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841E (5 July 2024); https://doi.org/10.1117/12.3032898
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KEYWORDS
Data modeling
Feature extraction
Classification systems
Deep learning
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