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Analysis of Various Tools of Natural Language Processing Based on Developers Perspective

Jatin Tak, Atrakesh Pandey


The subject of natural language method has visible mind-blowing development in current years, with neural network community substitution numerous of the traditional structures. A subset of artificial intelligence known as “natural language processing”, or NLP, analyses, comprehends, and generates natural human languages so that computers can reuse written and spoken human language without resorting to computer-generated language. Semantics and syntax are used in natural language processing, which is sometimes referred to as “computational linguistics”, to assist computers comprehend how people speak and write and how to derive meaning from what they say. In text to speech or speech to text, signature recognition, language detection and translation between natural language are enhanced in past two decades. Sequence prediction by taking input as sequences of amino acids can be used. We have a tendency to suggest a unified neural specification and gaining knowledge of algorithmic rule that is capable of be implemented to numerous natural language method obligations. During this survey paper, we have a tendency to study at the technique of appearing on a file with voice commands (speech to textual content). This survey paper analyses the various tools used of natural language processing based on developer perceptive. In future perspective, there are many scopes on enhancement in natural language processing technique and there deployment in science and society.


Natural language processing, digital assistant, computer-generated language, human computer interfaces, speech reputation

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Belinkov Y, Glass J. Analysis methods in neural language processing: A survey. Transactions of the Association for Computational Linguistics. 2019 Aug 1; 7: 49–72.

Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. J Mach Learn Res. 2011; 12(ARTICLE): 2493–537.

Kalyanathaya KP, Akila D, Rajesh P. Advances in natural language processing–a survey of current research trends, development tools and industry applications. Int J Recent Technol Eng. 2019 Feb; 7(5C): 199–202.

Jain A, Kulkarni G, Shah V. Natural language processing. Int J Comput Sci Eng. 2018; 6(1): 161–167.

Dale R. The commercial NLP landscape in 2017. Nat Lang Eng. 2017 Jul; 23(4): 641–7.

McCallum AK. Mallet: A machine learning for language toolkit. http://mallet. cs. umass. edu. 2002.

Pandey A, Jain R. 1–4D Protein Structures Prediction Using Machine Learning and Deep Learning from Amino Acid Sequences. InProceedings of the Third International Conference on Information Management and Machine Intelligence 2023. Springer, Singapore, 615–621.

Pandey A, Pant D, Gupta KK. A Novel Approach on Color Image Refocusing and Defocusing. Int J Comput Appl. 2013 Jan 1; 73(3).

Wagner W. Steven bird, ewan klein and edward loper: Natural language processing with python, analyzing text with the natural language toolkit. Lang Resour Eval. 2010 Dec; 44(4): 421–424.

Kumar E. Natural Language Processing; IK International Pvt. Ltd.: New Delhi, India. 2011.



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