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Nature of Computation and Communication. 7th EAI International Conference, ICTCC 2021, Virtual Event, October 28–29, 2021, Proceedings

Research Article

The Impacts of the Contextual Substitutions in Vietnamese Micro-text Augmentation

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  • @INPROCEEDINGS{10.1007/978-3-030-92942-8_3,
        author={Huu-Thanh Duong and Trung-Kiet Tran},
        title={The Impacts of the Contextual Substitutions in Vietnamese Micro-text Augmentation},
        proceedings={Nature of Computation and Communication. 7th EAI International Conference, ICTCC 2021, Virtual Event, October 28--29, 2021, Proceedings},
        proceedings_a={ICTCC},
        year={2022},
        month={1},
        keywords={Data augmentation Deep learning Sentiment analysis Contextual substitution},
        doi={10.1007/978-3-030-92942-8_3}
    }
    
  • Huu-Thanh Duong
    Trung-Kiet Tran
    Year: 2022
    The Impacts of the Contextual Substitutions in Vietnamese Micro-text Augmentation
    ICTCC
    Springer
    DOI: 10.1007/978-3-030-92942-8_3
Huu-Thanh Duong1,*, Trung-Kiet Tran2
  • 1: Faculty of Information Technology
  • 2: Department of Fundamental Studies
*Contact email: thanh.dh@ou.edu.vn

Abstract

The deep learning models rely on a huge amount of annotated training data to learn multiple layers of the features or representations and also avoid overfitting. However, the annotated dataset is unavailable, especially for the low resource languages. Building them is a tedious, time-consuming and expensive task. Thus, data augmentation has been mentioned as a perfect approach to generate the annotated data from the limited data without user intervention. In this paper, we evaluate the importances and the impacts of the contextual words to enhance the training data based on a pre-trained model which we build based on the reviews extracting the e-commerce websites in Vietnamese. We experiment on the sentiment analysis problem to evaluate the effectiveness of our approach.

Keywords
Data augmentation Deep learning Sentiment analysis Contextual substitution
Published
2022-01-03
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-92942-8_3
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