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

Phyu, Myat Sapal; Nwet, Khin Thandar

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

http://hdl.handle.net/20.500.12678/0000004596

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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