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A Study on a Joint Deep Learning Model for Myanmar Text Classification

http://hdl.handle.net/20.500.12678/0000004596
f8ff625a-0c62-4ac0-ba4b-43ad5f223412
95307a2b-c5f4-4498-aaa3-028c4e1a405d
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A A Study on a Joint Deep Learning Model for Myanmar Text Classification.pdf (600 Kb)
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Title
Title A Study on a Joint Deep Learning Model for Myanmar Text Classification
Language en
Publication date 2020-02-28
Authors
Phyu, Myat Sapal
Nwet, Khin Thandar
Description
Text classification is one of the most criticalareas of research in the field of natural languageprocessing (NLP). Recently, most of the NLP tasksachieve remarkable performance by using deeplearning models. Generally, deep learning modelsrequire a huge amount of data to be utilized. Thispaper uses pre-trained word vectors to handle theresource-demanding problem and studies theeffectiveness of a joint Convolutional Neural Networkand Long Short Term Memory (CNN-LSTM) forMyanmar text classification. The comparativeanalysis is performed on the baseline ConvolutionalNeural Networks (CNN), Recurrent Neural Networks(RNN) and their combined model CNN-RNN.
Keywords
text classification, CNN, RNN, CNNRNN, CNN-LSTM, deep learning model
Identifier 978-1-7281-5925-6
Journal articles
Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020)
Conference papers
Books/reports/chapters
Thesis/dissertations
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