Natural Language Processing Techniques for Information Retrieval Enhancing Search Engines with Semantic Understanding

¹ Assistant Professor, Department of Artificial Intelligence and Data Scienct, R.M.K. College of Engineering and Technology (RMKCET), Thiruvallur, Tamil Nadu, India
² Professor & Head, Dept of Computer Science and Engineering, St. Peter's Institute of Higher Education and Research, Chennai, Tamil Nadu, India
³ Assistant Professor, Department of Computer Science and Engineering (Cyber Security), CVR College of Engineering, Hyderabad, Telangana, India
⁴ Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
⁵ Assistant Professor, Department of Computer Science and Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
⁶ Assistant Professor, Department of IT, New Prince Shri Bhavani College of Engineering and Technology Chennai, Tamil Nadu, India

subiads@rmkcet.ac.in
bshanthini@gmail.com
Silparajm@gmail.com
shekar.kukunoor@mlrinstitutions.ac.in
keerthanag@jjcet.ac.in
anitha.it@npsbcet.edu.in

Abstract

This paper investigates new Natural Language Processing (NLP) methods which seek to improve information retrieval systems via semantic knowledge and focuses on enhancing search engines. The proposed ideas focus on reducing the size of the model (one of the biggest problems with large models), training it on domain-specific knowledge (the right knowledge is important for the real application) and ways to efficiently deal with unstructured data (this is also a key issue against NLP frameworks). The study highlights the need for hybrid models that combine generalization and specificity, fast algorithms for big data sets, and automated knowledge extraction. They include cross-lingual approaches, rapid learning in out-of-distribution domains, and human-centered design of AI systems. The end objective of this work is to create a semantic search engine which is adaptive, scalable and flexible; intent aware, and query ambiguity tolerant; improving semantic richness in results tailored to datasets of varying size; hence promising complementary applications of Natural Language Processing to information retrieval.