Hierarchical LSTM network for text classification

• Published in: SN applied sciences Vol. 1; no. 9; p. 1124
• Main Authors: Borna, Keivan, Ghanbari, Reza
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-09-2019; Springer Nature B.V
• Subjects: Algorithms; Applied and Technical Physics; Artificial intelligence; Artificial neural networks; Big Data and Applied Deep Learning: From Science to Applications; Category management; Chemistry/Food Science; Classification; Datasets; Earth Sciences; Engineering; Engineering: Data Science; Environment; Language; Machine learning; Materials Science; Methods; Natural language processing; Neural networks; Recurrent neural networks; Research Article; Text categorization; Computer science; Hierarchical attention network; Machine learning; Text classification
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-019-1165-1

Text classification has always been an important and practical issue so that we need to use the computer to classify and discover the information in the text. If we want to recognize the offending words in a text without human intervention, we should use this. In this article we will compare recurrent neural networks, convolutional neural networks and hierarchical attention networks with detailed information about each of which. We will represent a HAN model using Theano framework, which indicates more accurate validation for large datasets. For text classification problem in large datasets, we will use hierarchical attention networks to get a better result.