Abstract
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 explosions require extensive studies as well as more advanced methods in order to better understand and effectively model/learn these high-dimensional/structure-complicated textual datasets. Moving along with the recent progresses in deep learning and textual representation learning approaches, many researchers in this domain have been attracted by utilizing different deep neural architectures for learning essential features from texts. These novel neural architectures must enable to handle complex textual feature engineering. Moreover, it also has to be able to extract deeper semantic and structural information from textual resources. Recently, there are several integrations between advanced deep learning architectures, such as recurrent neural networks (RNNs), sequence-to-sequence (seq2seq) and transformers in text classification have been proposed. These hybrid deep neural architectures have shed light on how computers can comprehensively process sequential information from texts to fine-tune for leveraging the performance of multiple tasks in natural language processing, including classification. However, most of recent RNN-based techniques still suffer from several limitations. These limitations are mainly related to the capability of capturing the global long-range dependent as well syntactical structures of the given text corpus. There are some recent studies have shown that a combination of graph-based text representation and graph neural network (GNN) approaches can cope with these challenges. In this survey works, we mainly focus on discussing about recent state-of-the-art studies which are mainly dedicated on the text graph representation learning through GNN, named as TG-GNN. In addition, beside the TG-GNN based models’ features and capability discussions, we also mentioned about the pros/cons. Extensive comparative studies of TG-GNN based techniques in benchmark datasets for text classification problem are also provided in this survey. Finally, we highlight existing challenges as well as identify perspectives which might be useful for future improvements in this research direction.