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Comparative Analysis of Deep Learning Models for Myanmar Text Classification
http://hdl.handle.net/20.500.12678/0000006851
http://hdl.handle.net/20.500.12678/00000068515a63b23b-1c92-40c8-a9fc-c9cbfb498336
d24425da-272c-49fe-bd1c-8832adf49230
Publication type | ||||||
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Comparative Analysis of Deep Learning Models for Myanmar Text Classification | |||||
Language | en | |||||
Publication date | 2020-12-15 | |||||
Authors | ||||||
Myat Sapal Phyu | ||||||
Khin Thandar Nwet | ||||||
Description | ||||||
Text classification is one of the important research areas in Natural Language Processing (NLP). Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM) and their combination models have been applied in many NLP tasks. In this paper, we present a joint CNN with no max-polling layer and Bidirectional LSTM to fulfill the requirements of each model. The pro-posed model takes advantage of CNN to extract features and Bi-LSTM to capture long term contextual information from past and future contexts. The proposed model is compared with CNN, Bi-LSTM, RNN, and CNN-LSTM models with pre-trained word embedding on six article datasets in Myanmar language. | ||||||
Keywords | ||||||
Text classification, Myanmar Language, deep learning, Pre-trained word embedding, CNN, RNN, CNN-RNN, CNN-LSTM, Bi-LSTM | ||||||
Identifier | 10.1007/978-3-030-41964-6_7 | |||||
Conference papers | ||||||
ACIIDS | ||||||
4 March, 2020 | ||||||
Asian Conference on Intelligent Information and Database Systems | ||||||
Phuket, Thailand | ||||||
https://link.springer.com/chapter/10.1007%2F978-3-030-41964-6_7 |