Review of Graph Neural Network in Text Classification
Masoud Malekzadeh, Parisa Hajibabaee, Maryam Heidari, Samira Zad, Özlem Uzuner, James H. Jones
Abstract
Text classification is one of the fundamental problems in Natural Language Processing (NLP). Several research studies have used deep learning approaches such as Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification. Over the past decade, graph-based approaches have been used to solve various NLP tasks including text classification. This paper reviews the most recent state-of-the-art graph-based text classification, datasets, and performance evaluations versus baseline models.
Topics & Concepts
Computer scienceArtificial intelligenceRecurrent neural networkGraphText graphNatural language processingDeep learningArtificial neural networkConvolutional neural networkConvolution (computer science)Machine learningText miningTheoretical computer scienceText and Document Classification TechnologiesAdvanced Text Analysis TechniquesTopic Modeling