A Comparative Study of Deep Learning Models for Natural Language Processing (NLP)

Abstract

Natural language processing (NLP) has become an indispensable tool across many disciplines, and deep learning models have shown promising early results in improving the accuracy and efficiency of NLP-related tasks. In order to get valuable insights into the strengths and weaknesses of different models and approaches, and to help determine which models are the most successful for fulfilling particular NLP tasks, a comparative study of deep learning models for NLP is invaluable. Several deep learning models for NLP tasks including sentiment analysis, named entity recognition, and machine translation are compared and contrasted in this article's literature review. This research takes a look at popular benchmarks and data sets for evaluating deep learning models for NLP comparisons. (NLP). The strengths and weaknesses of various models and approaches are also highlighted throughout the examination. In addition to a discussion of recent advancements in the field such pretrained language models and attention processes, the article also details the many challenges and limitations of comparing deep learning models for NLP and how they stack up against one another. (NLP). The report concludes with a discussion of directions in which further study of the topic may go. There is a need to construct more interpretable and multilingual deep learning models, and there is also a need to explore cross-modal learning and domain-specific models. When taken as a whole, a research comparing different deep learning models for NLP might have far-reaching effects on the creation of new NLP applications and the enhancement of current ones. This is due to its capacity to aid in the creation of more precise and efficient models for natural language processing and to provide light on the relative merits of existing approaches.