Natural language processing: The current state of the art and challenges

Mohammad Mustafa Taye (), Rawan Abulail (), Belal Al-Ifan () and Fadi Alsuhimat ()

Review of Computer Engineering Research, 2024, vol. 11, issue 4, 155-187

Abstract: The use of deep learning techniques in natural language processing (NLP) is examined thoroughly in this study with particular attention to tasks where deep learning has been shown to perform very effectively. The primary strategies explored are phrase embedding, function extraction, and textual content cleaning. These are all essential for sorting textual content statistics and files. It appears in important gear like software programs, hardware and extensively used libraries and cutting-edge programs for deep learning in NLP. In NLP, deep learning is turning into a chief fashion, changing many areas and making large modifications in many fields. This paper stresses how deep analyzing techniques have a good-sized-ranging effect and how vital they may be for shifting the world in advance. This paper also discusses how deep learning may assist in solving modern issues and handling challenging and stressful situations in NLP research. Since those methods are getting more popular, it indicates that they're top at handling many NLP responsibilities. The final part of the evaluation talks about the most current makes use of, developing traits and long-term troubles in NLP. It helps practitioners and lecturers determine and use the capabilities of deep learning in the dynamic field of natural language processing with its applicable facts and examples.

Keywords: Deep learning; Feature extraction; Formatting; Machine learning; Natural language processing; NLP applications; Word embedding. (search for similar items in EconPapers)
Date: 2024
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