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CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification
Joint Authors
Zhao, Huan
Samatin Njikam, Aboubakar Nasser
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-17
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper introduces an extremely lightweight (with just over around two hundred thousand parameters) and computationally efficient CNN architecture, named CharTeC-Net (Character-based Text Classification Network), for character-based text classification problems.
This new architecture is composed of four building blocks for feature extraction.
Each of these building blocks, except the last one, uses 1 × 1 pointwise convolutional layers to add more nonlinearity to the network and to increase the dimensions within each building block.
In addition, shortcut connections are used in each building block to facilitate the flow of gradients over the network, but more importantly to ensure that the original signal present in the training data is shared across each building block.
Experiments on eight standard large-scale text classification and sentiment analysis datasets demonstrate CharTeC-Net’s superior performance over baseline methods and yields competitive accuracy compared with state-of-the-art methods, although CharTeC-Net has only between 181,427 and 225,323 parameters and weighs less than 1 megabyte.
American Psychological Association (APA)
Samatin Njikam, Aboubakar Nasser& Zhao, Huan. 2020. CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification. Journal of Electrical and Computer Engineering،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1184046
Modern Language Association (MLA)
Samatin Njikam, Aboubakar Nasser& Zhao, Huan. CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification. Journal of Electrical and Computer Engineering No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1184046
American Medical Association (AMA)
Samatin Njikam, Aboubakar Nasser& Zhao, Huan. CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification. Journal of Electrical and Computer Engineering. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1184046
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1184046