Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …

Text Classification is the most essential and fundamental problem in Natural Language
Processing. While numerous recent text classification models applied the sequential deep …

Text classification is one of the fundamental problems in Natural Language Processing
(NLP). Several research studies have used deep learning approaches such as Convolution …

Text classification is a fundamental task with broad applications in natural language
processing. Recently, graph neural networks (GNNs) have attracted much attention due to …

We propose a novel hierarchical graph-based text classification framework that leverages
the power of contextual node embedding and BERT-based dynamic fusion to capture the …

Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks …

Graph convolutional networks (GCNs), which are capable of effectively processing graph-
structural data, have been successfully applied in text classification task. Existing studies on …

Recently, lots of studies have explored text classification methods based on graph
convolutional neural network (GCN) technology. Compared with traditional deep learning …

Recently, researches have explored the graph neural network (GNN) techniques on text
classification, since GNN does well in handling complex structures and preserving global …

We consider the problem of learning efficient and inductive graph convolutional networks for
text classification with a large number of examples and features. Existing state-of-the-art …