A text classification method based on LSTM and graph attention network
Text classification is a popular research topic in the natural language processing. Recently solving text classification problems with graph neural network (GNN) has received increasing attention. However, current graph-based studies ignore the hidden information in text syntax and sequence structur...
Saved in:
| Main Authors: | Haitao Wang, Fangbing Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2128047 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Text classification model based on GNN and attention mechanism
by: ZENG Shuifei, et al.
Published: (2025-05-01) -
Graph-to-Text Generation with Bidirectional Dual Cross-Attention and Concatenation
by: Elias Lemuye Jimale, et al.
Published: (2025-03-01) -
Heterogeneous Graph Neural Network with Multi-View Contrastive Learning for Cross-Lingual Text Classification
by: Xun Li, et al.
Published: (2025-03-01) -
Enhanced HS Code Classification for Import and Export Goods via Multiscale Attention and ERNIE-BiLSTM
by: Mengjie Liao, et al.
Published: (2024-11-01) -
Enhancing Image Classification using Graph Attention Networks
by: Hasan Maher Ahmed
Published: (2025-08-01)