Abstract
This paper presents an application of Recurrent Neural Network for retrieving text from a given sequence of words of Gitanjali dataset. A Recurrent Neural Network (RNN) is trained to predict the poem corresponding to which the sequence of words are given. We demonstrate the experiment with two major RNN architectures and state the results to show which hyper-parameters like RNN size, sequence length, number of stacked layers affect the RNN most while completely memorizing the content of the poem. We also state the challenges to train the model in both forward and backward ways. We largely emphasis on the memorizing capability of RNN and put forward an application which depends on it.
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References
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv:1406.1078
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kamijo, K.I., Tanigawa, T.: Stock price pattern recognition-a recurrent neural network approach. In: 1990 IJCNN International Joint Conference on Neural Networks, pp. 215–221. IEEE (1990)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: Proceedings, p. 89. Presses universitaires de Louvain (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv:1301.3781
Olah, C.: Understanding LSTM networks. GITHUB blog. Accessed 27 Aug 2015
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310–1318 (2013)
Pouransari, H., Ghili, S.: Deep learning for sentiment analysis of movie reviews. Technical Report, Stanford University (2014)
Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)
Sproat, R., Jaitly, N.: RNN approaches to text normalization: a challenge (2016). arXiv:1611.00068
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Tagore, R., et al.: Gitanjali: Kītāñcali. Sura Books (2005)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164. IEEE (2015)
Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 2: short papers), vol. 2, pp. 707–712 (2015)
Wiszniowski, J., Plesiewicz, B., Trojanowski, J.: Application of real time recurrent neural network for detection of small natural earthquakes in poland. Acta Geophysica 62(3), 469–485 (2014)
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation (2016). arXiv:1609.08144
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Bhowmick, R.S., Sil, J. (2020). Memorizing and Retrieving of Text Using Recurrent Neural Network—A Case Study on Gitanjali Dataset. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_35
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DOI: https://doi.org/10.1007/978-981-13-9042-5_35
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