Hierarchical LSTM network for text classification

Ghanbari Amoughin R (2019)

Publication Language: English

Publication Type: Journal article

Publication year: 2019


Pages Range: 1-4

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.

Authors with CRIS profile

How to cite


Ghanbari Amoughin, R. (2019). Hierarchical LSTM network for text classification. SN Applied Sciences, 1-4. https://dx.doi.org/10.1007/s42452-019-1165-1


Ghanbari Amoughin, Reza. "Hierarchical LSTM network for text classification." SN Applied Sciences (2019): 1-4.

BibTeX: Download