Natural Language Processing-Based Intelligent Information Retrieval System Using Transformer Models
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Abstract
The rapid growth of digital information has increased the demand for intelligent information retrieval systems capable of understanding user intent and delivering semantically relevant results. Traditional keyword-based retrieval methods often fail to capture contextual meaning and complex natural language queries. This study proposes a Natural Language Processing-Based Intelligent Information Retrieval System (NLP-IIRS) using advanced Transformer models, including BERT, RoBERTa, DistilBERT, SBERT, T5, GPT-based encoders, and Retrieval-Augmented Generation (RAG). The framework integrates semantic indexing, dense vector retrieval, hybrid ranking, knowledge graphs, and explainable AI to improve retrieval performance. Experimental evaluation on a large multilingual dataset demonstrates improvements in retrieval accuracy, semantic relevance, query efficiency, and user satisfaction over conventional approaches. The findings indicate that Transformer-based intelligent retrieval systems provide an effective foundation for next-generation semantic search, while future research should address scalability, multilingual adaptation, privacy, and responsible AI deployment.
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1. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 4171–4186. https://doi.org/10.18653/v1/N19-1423
2. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692. https://arxiv.org/abs/1907.11692
3. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2020). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper, and lighter. Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing. https://arxiv.org/abs/1910.01108
4. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), 3982–3992. https://doi.org/10.18653/v1/D19-1410
5. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
6. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W. T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
7. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
8. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
9. Ijteba Sultana, Dr. Mohd Abdul Bari ,Dr. Sanjay,” Routing Performance Analysis of Infrastructure-less Wireless Networks with Intermediate Bottleneck Nodes”, International Journal of Intelligent Systems and Applications in Engineering, ISSN no: 2147-6799 IJISAE,Vol 12 issue 3, 2024, Nov 2023
10.
11. Jurafsky, D., & Martin, J. H. (2025). Speech and language processing (3rd ed., draft). Pearson. https://web.stanford.edu/~jurafsky/slp3/
12. Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval, 3(4), 333–389. https://doi.org/10.1561/1500000019
13. Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. McGraw-Hill.
14. Hugging Face. (2024). Transformers documentation. https://huggingface.co/docs/transformers
15. Elastic. (2024). Elasticsearch reference guide. https://www.elastic.co/guide/
16. Dr. Mohammed Abdul Bari,Arul Raj Natraj Rajgopal, Dr.P. Swetha ,” Analysing AWSDevOps CI/CD Serverless Pipeline Lambda Function's Throughput in Relation to Other Solution”, International Journal of Intelligent Systems and Applications in Engineering , JISAE, ISSN:2147-6799, Nov 2023, 12(4s), 519–526
17. Johnson, J., Douze, M., & Jégou, H. (2019). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535–547. https://doi.org/10.1109/TBDATA.2019.2921572
18. Meta AI. (2024). FAISS documentation. https://faiss.ai/
19. Apache Software Foundation. (2024). Apache Spark documentation. https://spark.apache.org/docs/latest/
20. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
21. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
22. National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.
23. ISO/IEC. (2023). ISO/IEC 23894:2023 Information technology—Artificial intelligence—Risk management. International Organization for Standardization.
24. Organisation for Economic Co-operation and Development. (2019). OECD principles on artificial intelligence. OECD Publishing.
25. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778
26. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
27. Hogan, A., Blomqvist, E., Cochez, M., D’Amato, C., Melo, G. D., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngonga Ngomo, A. C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37. https://doi.org/10.1145/3447772
28. Afsha Nishat, Dr. Mohd Abdul Bari, Dr. Guddi Singh,” Mobile Ad Hoc Network Reactive Routing Protocol to Mitigate Misbehavior Node”, International Journal Of Intelligent Systems And Applications In Engineering, IJUSEA, ISSN:2147-6799, Nov 2023
29. Performance Analysis of Wireless Sensor Networks for Smart Monitoring Applications. (2016). International Journal of Humanities and Information Technology, 1(04), 10–29. doi:10.21590/ijhit.01.04.04
30. Sannidhanam, A. H. (2021). Real-Time Claims Processing Using Event-Driven Architectures. International Journal of Technology, Management and Humanities, 7(01), 36–50. doi:10.21590/07.01.02
31. Anjani Haritha Sannidhanam. (2024). Guardrails and Safety Mechanisms for LLM-Powered Enterprise Applications. International Journal of Engineering Science & Humanities, 14(3), 273–285.
32. Sannidhanam, A. H. (2026). LLM-Driven Workflow Optimization: Intelligent Routing in Asynchronous Distributed Systems. International Journal of AI and Machine Learning, 1(2), 3–33.
33. Distributed Data Processing Frameworks for Large-Scale Healthcare Analytics. (2019). International Journal of Advance Industrial Engineering, 7(04), 1–10. doi:10.14741/ijaie/v.7.4.01
34. Anjani Haritha Sannidhanam. (2023). Zero-Downtime AI Model Updates in Real-Time Inference Systems. International Journal of Engineering Science & Humanities, 13(2), 70–78.