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, vol. 7, no. 6, Dec. 2023

APA:
Chiranjeevi Bura (2023). AI and Machine Learning Approaches for Efficient Document Retrieval. Iconic Research And Engineering Journals, 7(6).

MLA:
Chiranjeevi Bura "AI and Machine Learning Approaches for Efficient Document Retrieval" Iconic Research And Engineering Journals, vol. 7, no. 6, Dec. 2023.

BibTeX

@article{1707407,
author = {Chiranjeevi Bura},
title = {AI and Machine Learning Approaches for Efficient Document Retrieval},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {6},
pages = {461-469},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707407.pdf},
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},
month = {December}
}