Abstract
In this paper, we proposed an automatic convolutional neural network (CNN)-based method to classify poems written in Marathi, one of the popular Indian languages. Using this classification, a person unaware of Marathi Language can come to know what kind of emotion the given poem indicates. To the best of our knowledge, this is probably the first attempt of deep learning strategy in the field of Marathi poem classification. We conducted experiments with different models of CNN, considering different batch sizes, filter sizes, regularization methods like dropout, early stopping. Experimental results witness that our proposed approach outperforms both in effectiveness and efficiency. Our proposed CNN architecture for the classification of poems produces an impressive accuracy of 73%.
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Deshmukh, R., Kiwelekar, A.W. (2022). Deep Convolutional Neural Network Approach for Classification of Poems. In: Kim, JH., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2021. Lecture Notes in Computer Science, vol 13184. Springer, Cham. https://doi.org/10.1007/978-3-030-98404-5_7
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