Impact of Deep Learning on Natural Language Processing
- Authors: Arun Kumar Singh1, Ashish Tripathi2, Sandeep Saxena3, Pushpa Choudhary4, Mahesh Kumar Singh5, Arjun Singh6
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View Affiliations Hide AffiliationsAffiliations: 1 Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, India 2 School of Computing Science and Engineering, Galgotias University, Greater Noida, India 3 School of Computing Science and Engineering, Galgotias University, Greater Noida, India 4 School of Computing Science and Engineering, Galgotias University, Greater Noida, India 5 Dronacharya Group of Institutions, Greater Noida 201306, Uttar Pradesh, India 6 Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, India
- Source: Artificial Intelligence, Machine Learning and User Interface Design , pp 54-75
- Publication Date: May 2024
- Language: English
Impact of Deep Learning on Natural Language Processing, Page 1 of 1
< Previous page | Next page > /docserver/preview/fulltext/9789815179606/chapter-3-1.gifIn the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). "Natural Language" refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength.Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.
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