CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification
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...
Saved in:
| Main Authors: | Aboubakar Nasser Samatin Njikam, Huan Zhao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9701427 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification
by: Tianshi Wang, et al.
Published: (2020-01-01) -
LWheatNet: a lightweight convolutional neural network with mixed attention mechanism for wheat seed classification
by: Xiaojuan Guo, et al.
Published: (2025-01-01) -
Techno-economic aspects of concrete lightweighting by char enrichment with phosphates from wastewater
by: Josef Marousek, et al.
Published: (2025-05-01) -
Lung Segmentation with Lightweight Convolutional Attention Residual U-Net
by: Meftahul Jannat, et al.
Published: (2025-03-01) -
Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces
by: Sameh Zarif, et al.
Published: (2025-01-01)