Advancements in Natural Language Processing for Text Understanding

¹ Assistant Professor, Department of Computer Science and Engineering(Specialization)School of Engineering & TechnologyJain University, Bangalore - 562112 Karnataka, India
² Assistant Professor, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai 48
³ Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
⁴ College of technical engineering, The Islamic university, Najaf, Iraq
⁵ Tashkent State Pedagogical University, Tashkent, Uzbekistan

^* Correspondingauthor: mj.basha@jainuniversity.ac.in
jayashankari_it@psvpec.in
ahmedalawadi@iunajaf.edu.iq
durdonapulatova88@gmail.com

Abstract

Natural language processing (NLP) developments have made it possible for robots to read and analyze human language with astounding precision, revolutionizing the field of text understanding. An overview of current advancements in NLP approaches and their effects on text comprehension are provided in this abstract. It examines significant developments in fields including named entity identification, sentiment analysis, semantic analysis, and question answering, highlighting the difficulties encountered and creative solutions put forth. To sum up, recent developments in natural language processing have raised the bar for text comprehension. Deep learning models and extensive pre-training have changed methods including semantic analysis, sentiment analysis, named entity identification, and question answering. These developments have produced text comprehension systems that are increasingly precise and complex. However, issues with prejudice, coreference resolution, and contextual comprehension still need to be resolved. The future of NLP for text understanding has considerable potential with continuing study and innovation, opening the door for increasingly sophisticated applications in numerous sectors.