Evolution of natural language processing methods
https://doi.org/10.17726/philIT.2024.2.4
Abstract
Natural language processing (NLP) has undergone significant changes in its methods, reflecting advances in computing technology and cognitive research. This article reviews the key stages of the evolution of natural language processing methods. The article touches on the topic of the first NLP systems developed, provides justification for the reasons for the complexity of some processed texts and the possible depth of analysis. In addition, it describes not only NLP methods before and after the GPT revolution, but also current trends and prospects in the field of natural language processing. The article allows us to trace how the idea of natural language text has changed during the development of computer analysis methods, as well as to understand what text is in the mirror of natural language processing, what is really the subject of natural language processing research and what cannot be seen through the eyes of a simple researcher who does not use NLP methods.
About the Author
A. Yu. Besedina
Pyatigorsk State University
Russian Federation
Russian Federation
Besedina Anastasia Yuryevna, first-year graduate student, Department of Theory and Practice of Translation Institute of Translation Studies, Russian Studies and Multilingualism
Pyatigorsk
References
1. Bolshakova E. I., Klyshinsky E. S., Lande D. V., Noskov A.A., Peskova O. V., Yagunova E. V. Automatic text processing in natural language and computational linguistics: textbook. the manual. M.: MIEM, 2011. 272 p.
2. Goldberg J. Neural network methods in natural language processing. Translated from English by A.A. Slinkin. M.: DMK Press, 2019. 282 p.
3. Leontieva N. N. Automatic understanding of texts: systems, models, resources: a textbook. M.: IC “Academia”, 2006. 304 p.
4. Manning C. D., Schütze H. Foundations of Statistical Natural Language Processing, 8, [print.]-е изд. Cambridge, Massachusetts: The MIT Press, 2005. 720 p.
5. Bird S., Klein E., Loper E. Natural language processing with Python. 1st ed-е изд. Beijing; Cambridge [Mass.]: O’Reilly, 2009. 479 c.
6. Tyurina D.A., Palmov S. V. Application of neural networks in natural language processing // Journal of Applied Research. 2023. № 7. P. 158-162.
7. Jackson P., Moulinier I. Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization // Computational Linguistics. 2003. № 29. P. 510-511.
8. Jelinek F. Statistical methods for speech recognition. Cambridge (GB): MIT press, 1998. 305 p.
9. Jurafsky D., Martin J. H. Speech and Language ProcessingAn Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 2023. 636 p.
10. Kumar L.A., Renukay D. K. Deep learning approach for natural language processing, speech, and computer vision: techniques and use cases. First edition-е изд., Boca Raton, FL: CRC Press, 2023. 246 p.
11. Garg M., Kumar S. Natural language processing and information retrieval: principles and applications. London, New York: CRC Press, 2024. 271 p.
12. Manning C. D., Raghavan P., Schütze H. Introduction to information retrieval. Cambridge: Cambridge university press, 2008. 528 p.
13. Everaert M. B. H., Huybregts M.A. C., Chomsky N., Berwick R. C., Bolhuis J. J. Structures, not strings: Linguistics as part of the cognitive sciences // Trends in Cognitive Sciences. 2015. № 19(12). P. 729-743.
14. Mihalcea R., Radev D. Graph-based natural language processing and information retrieval. Cambridge; New York: Cambridge University Press, 2011. 192 p.
15. Rothman D. Transformers for natural language processing and computer vision: explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3. Third edition, revised publication Sept 2024-е изд., Birmingham, UK Mumbai: Packt Publishing Ltd, 2024. 693 p.
Review
For citations:
Besedina A.Yu. Evolution of natural language processing methods. Philosophical Problems of IT & Cyberspace (PhilIT&C). 2024;(2):52-63. (In Russ.) https://doi.org/10.17726/philIT.2024.2.4
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