Abstract
Text classification is one of the major research areas for Natural Language Processing (NLP). Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and their combination models have been applied in many NLP tasks. This paper presents a joint CNN with no max-polling layer and Bidirectional LSTM to fulfill the requirements of each model. The proposed 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 five article datasets in Myanmar language.
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Acknowledgement
We deeply thank the anonymous reviewers for sharing their precious time to check our manuscript. We greatly thank the researchers who released pre-trained vectors publicly and these resources helpful for low resources languages. We greatly thank the friends who assist to collect and annotate Myanmar text datasets.
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Phyu, M.S., Nwet, K.T. (2020). Comparative Analysis of Deep Learning Models for Myanmar Text Classification. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_7
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