Advancements in natural language processing: Implications, challenges, and future directions

Abstract

This research delves into the latest advancements in Natural Language Processing (NLP) and their broader implications, challenges, and future directions. With the ever-increasing volume of text data generated daily from diverse sources, extracting relevant and valuable information is becoming more complex. Conventional manual techniques for handling and examining written information are laborious and susceptible to mistakes, underscoring the necessity for effective automated alternatives. The advancements in Natural Language Processing (NLP), namely in transformer-based models and deep learning techniques, have demonstrated considerable potential in improving the precision and consistency of various NLP applications. This work presents a novel approach that combines systematic review methods with sophisticated NLP approaches to enhance the overall efficiency of NLP systems. The proposed strategy guarantees an organized and clear literature review process, resulting in more informative and contextually relevant results. The report examines NLP's implications, problems, and opportunities, providing significant insights that are anticipated to propel improvements in NLP technology and its application in many industries.

Keywords

• Deep learning techniques
• Natural language processing
• Systematic review methodologies
• Text data analysis
• Transformer models