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Exploring the Power of Deep Learning in Natural Language Processing : A Comprehensive Review of Techniques, Applications, and Future Directions


Affiliations
1 Research Scholar, Department of Computer Science, Punjabi University, Patiala, India
2 Assistant Professor, Punjabi Computer Help Center, Punjabi University, Patiala, India
 

This paper provides a comprehensive review of the role of deep learning techniques in natural language processing (NLP). With the explosion of textual data in recent years, the need for efficient and accurate NLP algorithms has become increasingly important. Deep learning approaches, which are based on neural networks, have shown great potential in addressing many NLP tasks such as language modeling, sentiment analysis, text classification, and machine translation, among others. In this paper, we provide an overview of the key deep learning models that have been used in NLP, including recurrent neural networks (RNNs), convolutional neural networks (CNNs) and transformers. We also discuss the challenges and limitations associated with deep learning models, such as overfitting, data sparsity, and interpretability. Moreover, we review the recent advancements in deep learning techniques, including transfer learning and pre-training, which have enabled the development of state-of-the-art NLP models. Finally, we highlight some of the promising future directions in the field of deep learning for NLP, such as multi-task learning and the integration of symbolic reasoning with neural networks.

Keywords

Natural Language Processing, Deep Learning, Artificial Intelligence, Neural Networks, Language Modeling.
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  • Exploring the Power of Deep Learning in Natural Language Processing : A Comprehensive Review of Techniques, Applications, and Future Directions

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Authors

Gurpreet Singh
Research Scholar, Department of Computer Science, Punjabi University, Patiala, India
C. P. Kamboj
Assistant Professor, Punjabi Computer Help Center, Punjabi University, Patiala, India

Abstract


This paper provides a comprehensive review of the role of deep learning techniques in natural language processing (NLP). With the explosion of textual data in recent years, the need for efficient and accurate NLP algorithms has become increasingly important. Deep learning approaches, which are based on neural networks, have shown great potential in addressing many NLP tasks such as language modeling, sentiment analysis, text classification, and machine translation, among others. In this paper, we provide an overview of the key deep learning models that have been used in NLP, including recurrent neural networks (RNNs), convolutional neural networks (CNNs) and transformers. We also discuss the challenges and limitations associated with deep learning models, such as overfitting, data sparsity, and interpretability. Moreover, we review the recent advancements in deep learning techniques, including transfer learning and pre-training, which have enabled the development of state-of-the-art NLP models. Finally, we highlight some of the promising future directions in the field of deep learning for NLP, such as multi-task learning and the integration of symbolic reasoning with neural networks.

Keywords


Natural Language Processing, Deep Learning, Artificial Intelligence, Neural Networks, Language Modeling.

References