A Systematic Survey of Natural Language Processing (NLP) Approaches in Different Systems

Authors

  • N Vasunthira Devi Ph.D Research Scholar, Department of Computer Science, AVC College (Autonomous) Mannampandal, Mayiladuthurai
  • R Ponnusamy Professor, Department of Computer Science & Engineering, Sri Lakshmi Ammal Engineering College, Chennai

Keywords:

NLP, Education, Application, Education System, Electronic Healthcare Systems, Healthcare, NLP Techniques

Abstract

Natural Language Processing and Programming Languages are both established areas in the field of Computer Science, each of them with a long research tradition. Natural Language processing Frameworks can make conceivable the use of natural language to express ideas. In education system, Natural Language Processing gives solution in a assortment of differing fields related with the social and cultural context of language learning. Natural Language Processing is broadly integrated with the expansive number of education connections such as research, science, linguistics, e-learning, evaluations system, and contributes resulting positive outcomes in other education settings such as schools, higher education system, and universities. In healthcare, NLP Frameworks decrease fetched as well as improve the quality of electronic healthcare systems. It is therefore against this foundation that this paper reviews the NLP techniques, uses in education, healthcare and their applications as well as their limitations. The reason of this relook is to overview and report the current state and the future bearings of the use of NLP innovations in different frameworks in the corporate world.

References

Chetan Arora, Mehrdad Sabetzadeh, Lionel Briand & Frank Zimmer, “Automated Checking of Conformance to Requirements Templates Using Natural Language Processing”, in IEEE Transactions on Software Engineering, Vol-41, Issue: 10, PP. 944 – 968, 2015.

Henrik Leopold, Jan Mendling and Artem Polyvyanyy, “Supporting Process Model Validation through Natural Language Generation”, in IEEE Transactions on Software Engineering, Vol- 40, Issue-8, PP.818 – 840, 2015.

Julia Hirschberg, Bruce W. Ballard and Donald Hindle, “Natural language processing”, in AT&T Technical Journal, Vol- 67, Issue-1, PP. 41 – 57, 1988.

Erik Cambria and Bebo White, “Jumping NLP Curves: A Review of Natural Language Processing Research”, in IEEE Computational Intelligence Magazine, Vol-9, Issue-2, PP.48 – 57, 2014.

Michael T. Mills and Nikolaos G. Bourbakis, “Graph-Based Methods for Natural Language Processing and Understanding-A Survey and Analysis”, in IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol-44, Issue-1, PP.59 – 71, 2014.

T. Patten and P. Jacobs, “Natural-language processing”, in IEEE Expert, Vol- 9, Issue- 1, PP. 35, 1994.

Minhwa Chung and D. Moldevan, “Applying parallel processing to natural-language processing”, in IEEE Expert, Vol-9, Issue-1, PP. 36 – 44, 1994.

B. W. Wah and T. S. Huang, “Report on workshop on high performance computing and communications for grand challenge applications: computer vision, speech and natural language processing, and artificial intelligence”, in IEEE Transactions on Knowledge and Data Engineering, Vol- 5, Issue-1, PP. 138 – 154, 1993.

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Published

2025-11-11

How to Cite

[1]
N. Vasunthira Devi and R. Ponnusamy, “A Systematic Survey of Natural Language Processing (NLP) Approaches in Different Systems”, Int. J. Comp. Sci. Eng., vol. 4, no. 7, pp. 192–198, Nov. 2025.

Issue

Vol. 4 No. 7 (2016): IJCSE July Edition

Section

Survey Article

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