Short text classification is an important problem of natural language processing (NLP), and
graph neural networks (GNNs) have been successfully used to solve different NLP …

Aiming at the sparsity of short text features, lack of context, and the inability of word
embedding and external knowledge bases to supplement short text information, this paper …

In many studies, the graph convolution neural networks were used to solve different natural
language processing (NLP) problems. However, few researches employ graph …

Short text classification is a fundamental problem in natural language processing, social
network analysis, and e-commerce. The lack of structure in short text sequences limits the …

Text classification is an important task in natural language processing. However, most of the
existing models focus on long texts, and their performance in short texts is not satisfied due …

Short text classification is a fundamental task in natural language processing. It is hard due
to the lack of context information and labeled data in practice. In this paper, we propose a …

The short text, sparse features, and the lack of training data, etc. are still the key bottlenecks
that restrict the successful application of traditional text classification methods. To address …

Some text classification methods don't work well on short texts due to the data sparsity.
What's more, they don't fully exploit context-relevant knowledge. In order to tackle these …

Graph convolutional network (GCN) has a strong ability to extract the global feature but
neglects the order of the words, thus leading to its weak effect on short text classification. In …

With the development of online interactive media platforms, a large amount of short text has
appeared on the internet. Determining how to classify these short texts efficiently and …