Open AccessA literature survey on recurrent attention learning for text classification
Author(s) -
Ayesha Mariyam,
SK Althaf Hussain Basha,
S. Viswanadha Raju
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1042/1/012030
Subject(s) - computer science , recurrent neural network , artificial intelligence , deep learning , task (project management) , convolutional neural network , machine learning , domain (mathematical analysis) , long short term memory , multi task learning , artificial neural network , natural language processing , speech recognition , mathematical analysis , mathematics , management , economics
Witha rapid rise of complex data every year needs more enrichment in machine learning methods to provide vigorous and accurate data classification. Deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory(LSTM) have accomplished to obtain better results in the domain of computer vision, object recognition, speech recognition and natural language processing compared to traditional machine learning algorithms. This paper mainly discusses about the blending of attention mechanism with various deep learning models for text classification which improves the performance of text classification task.