Computational linguistics at the crossroads: A comprehensive review of NLP advancements
1 Department of Enterprise Engineering, University of Rome Tor Vergata, Italy.
2 Department of English, Government College Women University, Sialkot, Pakistan.
3 Faculty of Asian and African Studies, Guangdong University of Foreign Studies, 510420, Guangzhou, Guangdong, China.
4 MS Computer Science, College of Computing and Digital Media (CDM), Depaul University, Chicago, United States of America.
5 School of Computer Science Chenab College of Advance Studies Faisalabad, Pakistan.
6 Department of Computer science, Institute of Southern Punjab, Multan, Pakistan.
7 Deputy Hostel Provost, Liaquat University of Medical & Health Sciences Jamshoro, Pakistan.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(02), 578–591.
Article DOI: 10.30574/wjaets.2024.11.2.0146
Publication history:
Received on 11 March 2024; revised on 20 April 2024; accepted on 23 April 2024
Abstract:
New NLP breakthroughs have put Computational Linguistics at a crossroads. NLP's past, present, and future are covered. This review explains computational linguistics' creation with a brief history of linguistics and computer science. Early solutions processed and understood natural language using rule-based systems using manually constructed linguistic rules. Over time, these tactics became increasingly problematic as language became more complex and obscure. Statistical approaches transformed operations. Neural network-based machine learning methods are leading the area because they can learn complicated patterns and representations from large text collections. A data-driven model revolution in natural language processing enhanced language modelling, machine translation, and sentiment analysis. Next, NLP improvements for several tasks and applications are evaluated. Language understanding models that capture semantic nuances and contextual relationships use deep learning frameworks. Word embeddings and transformer-based architectures like GPT and BERT perform well on benchmark datasets for text classification, question answering, and named item identification. The paper also shows how NLP interacts with computer vision, voice processing, and other domains to show the merits and cons of cross-disciplinary research. Multimodal techniques that combine text, graphics, and audio may increase natural language processing and interpretation. The review discusses NLP's effects on prejudice, justice, and privacy. Responsible development and implementation are needed when NLP technology becomes widespread due to algorithmic bias and data privacy concerns. NLP research directions and concerns are reviewed. Existing models may meet standards but fail in practice.
Keywords:
Computational Linguistics; Machine Learning; Language; Natural Language Processing; Cognitive Psychology
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