.png){kind=icon}
Return
Text Classification Using Document-Relational Graph Convolutional Networks
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3221820.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Graph Convolutional Networks (GCNs) have received considerable attention in the field of artificial machine intelligence (AMI) and natural language processing research because they can build more sophisticated accompanying graph structures than traditional neural networks for feature engineering. Graph is used as feature in neural network because it is easy to find relations among nodes. In text classification applications, a GCN can create complex and rich relation-based adjacent matrix graphs as features to be trained. The existing methods, on the other hand, only generated adjacent matrix graphs in GCN at the word-document and word-word levels as features. In this paper, we propose a document-relational GCN to achieve superior accuracy in text classification by adding cumulative term frequency-inverse document frequency (TF-IDF) document-document relations as features. The performance of the proposed method is evaluated using five popular benchmark databases. In addition, different hidden nodes and proportions of document-document features are tested to achieve an advantageous outcome.
Keywords:
Mathematical models
Deep learning
Social networking (online)
Neural networks
Convolutional neural networks
Vocabulary
Feature extraction
Graph neural networks
Text categorization
Text mining
Artificial neural networks
Natural language processing
NLP
document-document relation
graph convolutional network
text classification
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
Update on the Clinical Diagnosis, Management, and Pathogenesis of Pulmonary Alveolar Proteinosis (Phospholipidosis).png)
Chest
ChestIF0
PREAI
ShareSaveAn Integration Model Based on Graph Convolutional Network for Text ClassificationIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSave
View more