AI and Machine Learning Approaches for Efficient Document Retrieval

• Author(s): Chiranjeevi Bura
• Paper ID: 1707407
• Page: 461-469
• Published Date: 31-12-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 6 December-2023

Abstract

The exponential growth of digital repositories demands intelligent document retrieval beyond conventional indexing and keyword-based searches. Machine Learning (ML) techniques, particularly deep learning, neural ranking models, and reinforcement learning, enhance retrieval efficiency, scalability, and contextual understanding. This study explores ML- driven methodologies for document classification, ranking, and multimodal retrieval, integrating natural language processing (NLP) and transformer-based architectures. We analyze advancements in enterprise content management, legal document retrieval, and OCR-based processing, highlighting the superior- ity of deep learning over traditional search methods. Despite significant improvements, challenges persist in model scalability, explainability, and real-time retrieval. Future research should focus on optimizing federated learning for privacy-preserving search, enhancing explainable AI, and improving neural indexing for large-scale repositories.

Keywords

Machine Learning, Information Retrieval, Deep Learning, Enterprise Content Management, Transformer Models, Neural Ranking, NLP, Explainable AI

Citations

IRE Journals:
Chiranjeevi Bura "AI and Machine Learning Approaches for Efficient Document Retrieval" Iconic Research And Engineering Journals Volume 7 Issue 6 2023 Page 461-469

IEEE:
Chiranjeevi Bura "AI and Machine Learning Approaches for Efficient Document Retrieval" Iconic Research And Engineering Journals, 7(6)