The Triumvirate NLP, NLG and NLU- Effect on AI within the purview of Computational linguistics
DOI:
https://doi.org/10.61841/nyfkm734Keywords:
Natural Language Processing, Natural Language Generation, Natural language Understanding, Machine learning, Artificial intelligence, Deep learning, Computational linguisticsAbstract
This paper is a factual breakthrough in the field of artificial intelligence AI underlying natural language processing (NLP), natural language generation (NLG), and natural language understanding (NLU) that are productively reflected in pedagogical ways of innovation in several mushroom platforms through computational linguistics on a larger scale. Natural Language Processing, Natural Language Generation, and Natural Language Understanding is a way of analyzing multi-dimensional texts by computerized means within the AI environment of computational linguistics from a broader perspective in a dynamic way. NLP involves the gathering of knowledge on “how human beings understand and use language." This is done in order to develop appropriate tools and techniques that could make computer systems understand and manipulate natural languages to perform various desired tasks. This paper reviews the literature on NLP. It also covers or gives a hint about the history of NLP. It is based on document analysis. This research paper could be beneficial to those who wish to study and learn about NLP, NLG, and NLU within the sphere of AI, under the supreme banner of computational linguistics. We can simply reach the conclusion that this triumvirate overlaps among themselves to a great extent, such as machine learning and deep learning under the label of AI through computational linguistics.
Downloads
References
1. Adler, Meni, and Miki Tebeka. 2001. Unsupervised Hebrew part of speech tagging. In Shuly Wintner, editor, Israeli Seminar on Computational Linguistics (ISCOL’01), pages 19–20, Haifa, February.
2. Azar, Moche. 1972. Automatic syntactical analysis: the method and its application to the Book of Ruth. Hebrew Computational Linguistics, 5:1–50, February. In Hebrew.
3. Beesley, Ken. 1996. Arabic finite-state morphological analysis and generation. In Proceedings of COLING-96, the 16th International Conference on Computational Linguistics, Copenhagen.
4. C. Callaway and J. Lester. “Narrative Prose Generation." In: Articial Intelligence 139.2 (Aug. 2002), pp. 213–252. DOI: https://www.sciencedirect.com/science/article/PII/S0004370202002308?via%3Dihub.
5. Choueka, Yaacov. 1972. Fast searching and retrieval techniques for large dictionaries and concordances. Hebrew Computational Linguistics, 6:12–32, July. In Hebrew.
6. Dagan, Ido, and Alon Itai. 1994. Word sense disambiguation using a second language monolingual corpus. Computational Linguistics, 20(4):563–596, December.
7. Yang, Yezhou, et al.Corpus-guided sentence generation of natural images. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011
8. P. Jackson and I. Moulinier, "Natural Language Processing for Online Applications," Cambridge University Press, New York, 2012, pages 7-9. [15] R. Bose. “Natural language processing: Current state and future directions." International Journal of the Computer, the Internet, and Management Vol. 12#1 (January–April, 2004), pp. 1–11.
9. R. Grishman. Computational Linguistics: An Introduction. Studies in Natural Language Processing. Cambridge University Press, 1986.
10. E. Hovy. Pragmatics and Natural Language Generation. Artificial Intelligence, 43:153{197, 1990.
11. Neil McIntyre and Mirella Lapata. “Learning to Tell Tales: A Data-Driven Approach to Story Generation." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the
AFNLP: Volume 1-Volume 1. Association for Computational Linguistics, 2009, pp. 217–225.
12. Hugo Gonc¸alo Oliveira. “A Survey on Intelligent Poetry Generation: Languages, Features, Techniques, Reutilisation, and Evaluation." Proceedings of the 10th International Conference on Natural Language Generation. The 10th International Conference on Natural Language Generation. Santiago de Compostela, Spain: Association for Computational Linguistics, 2017, pp. 11–20
13. Frank Schilder, Blake Howald, and Ravi Kondadadi.2013. Gennext: A consolidated domain adaptable NLG system. In Proceedings of the 14th European Workshop on Natural Language Generation, pages 178–182, Sofia, Bulgaria, August. Association for Computational Linguistics.
14. Charese Smiley, Vassilis Plachouras, Frank Schilder, Hiroko Bretz, Jochen L. Leidner, and Dezhao Song. 2016. When to plummet and when to soar: corpus-based verb selection for natural language generation. In The 9th International Natural Language Generation Conference, page 36.
15. Ehud Reiter. 2007. An architecture for data-to-text systems. In Proceedings of the Fifth European Workshop on Natural Language Generation, ENLG ’07, pages 97–104, Stroudsburg, PA, USA. Association for Computational Linguistics.
16. Saad Mahamood and Ehud Reiter. 2011. Generating affective natural language for parents of neonatal infants. In Proceedings of the 13th European Workshop on Natural Language Generation, ENLG 2011, pages 12–21, Nancy, France. Association for Computational Linguistics.
17. Allen J. Natural Language Understanding, 2nd edn., Redwood City, CA Benjamin/Cummings, 1995
18. Baud, R. H., Alpay, L., & Lovis, C. (1994). Let’s Meet the Users with Natural Language Understanding. Knowledge and Decisions in Health Telematics: The Next Decade, 12, 103
19. T. Winograd, Procedures as a Representation for Data in a Computer Program for Understanding Natural Language, 1971, MIT-AI-TR-235
20. O. Bender, K. Macherey, F.J. Och, and H. Ney: Comparison of Alignment Templates and Maximum Entropy Models for Natural Language Understanding. In Proc. of the 10th Conf. of the European Chapter of the Association for Computational Linguistics (EACL), pp. 11–18, Budapest, Hungary, April 2003.
21. S. Della Pietra, M. Epstein, S. Roukos, T. Ward: Fertility Models for Statistical Natural Language Understanding. In Proc. of the 8th Conf. of the European Chapter of the Association for Computational Linguistics (EACL), pp. 168–173, Madrid, Spain, July 1997.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.