LA-HCN: label-based attention for hierarchical multi-label text classification neural network
Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, such as the local, global, or a combination of them. However,...
Main Authors: | Zhang, Xinyi, Xu, Jiahao, Soh, Charlie, Chen, Lihui |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/160673 |
Similar Items
-
Deep Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery
by: Aaliyah Alshehri, et al.
Published: (2019-01-01) -
Hierarchical Sequence-to-Sequence Model for Multi-Label Text Classification
by: Zhenyu Yang, et al.
Published: (2019-01-01) -
Label semantics embedding and hierarchical attentions for text representation learning
by: Min, Fuzhou
Published: (2023) -
Hierarchical Multi-Granularity Attention- Based Hybrid Neural Network for Text Classification
by: Zhenyu Liu, et al.
Published: (2020-01-01) -
MsCoa: Multi-Step Co-Attention Model for Multi-Label Classification
by: Haoyang Ma, et al.
Published: (2019-01-01)