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Bio-Inspired Algorithms in NLP Techniques: Challenges, Limitations and Its Applications.

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Název: Bio-Inspired Algorithms in NLP Techniques: Challenges, Limitations and Its Applications.
Autoři: Ho, Huu-Tuong, Nguyen, Thi-Thuy-Hoai, Huy, Duong Nguyen Minh, Nguyen, Luong Vuong
Zdroj: Computers, Materials & Continua; 2025, Vol. 83 Issue 3, p3945-3973, 29p
Témata: PARTICLE swarm optimization, MACHINE translating, NATURAL language processing, ANT algorithms, ARTIFICIAL neural networks, BIOLOGICALLY inspired computing, DEEP learning
Abstrakt: Natural Language Processing (NLP) has become essential in text classification, sentiment analysis, machine translation, and speech recognition applications. As these tasks become complex, traditional machine learning and deep learning models encounter challenges with optimization, parameter tuning, and handling large-scale, high-dimensional data. Bio-inspired algorithms, which mimic natural processes, offer robust optimization capabilities that can enhance NLP performance by improving feature selection, optimizing model parameters, and integrating adaptive learning mechanisms. This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—across core NLP tasks. We analyze their comparative advantages, discuss their integration with neural network models, and address computational and scalability limitations. Through a synthesis of existing research, this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP, offering insights into hybrid models and lightweight, resource-efficient adaptations for real-time processing. Finally, we outline future research directions that emphasize the development of scalable, effective bio-inspired methods adaptable to evolving data environments. [ABSTRACT FROM AUTHOR]
Copyright of Computers, Materials & Continua is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Computers, Materials & Continua; 2025, Vol. 83 Issue 3, p3945-3973, 29p
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  Data: <searchLink fieldCode="DE" term="%22PARTICLE+swarm+optimization%22">PARTICLE swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+translating%22">MACHINE translating</searchLink><br /><searchLink fieldCode="DE" term="%22NATURAL+language+processing%22">NATURAL language processing</searchLink><br /><searchLink fieldCode="DE" term="%22ANT+algorithms%22">ANT algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22ARTIFICIAL+neural+networks%22">ARTIFICIAL neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22BIOLOGICALLY+inspired+computing%22">BIOLOGICALLY inspired computing</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink>
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  Data: Natural Language Processing (NLP) has become essential in text classification, sentiment analysis, machine translation, and speech recognition applications. As these tasks become complex, traditional machine learning and deep learning models encounter challenges with optimization, parameter tuning, and handling large-scale, high-dimensional data. Bio-inspired algorithms, which mimic natural processes, offer robust optimization capabilities that can enhance NLP performance by improving feature selection, optimizing model parameters, and integrating adaptive learning mechanisms. This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—across core NLP tasks. We analyze their comparative advantages, discuss their integration with neural network models, and address computational and scalability limitations. Through a synthesis of existing research, this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP, offering insights into hybrid models and lightweight, resource-efficient adaptations for real-time processing. Finally, we outline future research directions that emphasize the development of scalable, effective bio-inspired methods adaptable to evolving data environments. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Computers, Materials & Continua is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.32604/cmc.2025.063099
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        Text: English
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      – SubjectFull: NATURAL language processing
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      – SubjectFull: BIOLOGICALLY inspired computing
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      – SubjectFull: DEEP learning
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              Text: 2025
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