Abstract
Natural language processing (NLP) is generally referred to as the utilization of natural languages such as text and speech through software. The NLP is studied for more than 50 years approximately. Deep learning (DL) is one of the subdomains of machine learning, which is motivated by functions of the human brain, also known as artificial neural network (ANN). DL is performed well on several problem areas, where the output and inputs are taken as analog. Also, deep learning achieves the best performance in the domain of NLP through the approaches. The approaches need additional data, however, not have as much linguistic expertise for operating and training. There are a large number of hype claims in the region of deep learning techniques. But, away from the hype, the deep learning techniques obtain better outcomes. In this paper, the information linked with the DL algorithm is analyzed based on the NLP approach. The concept behind the network implementation and feature learning is described clearly. Finally, the outline of various DL approaches is made concerning result validation from preceding models and points out the influence of deep learning models on NLP.
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Murthy, M.Y.B., Mastanbi, S., Sujitha, B., Babu, K.R. (2023). Evaluating Deep Learning Algorithms for Natural Language Processing. In: Kumar, S., Hiranwal, S., Purohit, S.D., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-3951-8_53
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DOI: https://doi.org/10.1007/978-981-19-3951-8_53
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