Abstract
Text classification has a growing interest among NLP researchers due to its tremendous availability on online platforms and emergence on various Web 2.0 applications. Recently, text classification in resource-constrained languages has been bringing much attention due to the sharp increase of digital resources. This paper presents a CNN based text classification model for one of the low resource languages like Bengali. The goal of the Bengali text classification is to assign a particular category to a text into one of the pre-defined categories based on its semantic and syntactic meaning. The proposed system comprises of four key modules: embedding model generation, Text to feature representation, training, and testing. The classification system trained and validated with 39, 079 and 6, 000 text datasets. Experimental evaluation with 9, 779 test datasets shows the accuracy of \(96.85\%\), which indicates the superior performance compared to the existing techniques.
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This work was supported by the University Grants Commission of Bangladesh.
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Hossain, M.R., Hoque, M.M., Sarker, I.H. (2021). Text Classification Using Convolution Neural Networks with FastText Embedding. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_11
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