AI and Machine Learning Approaches for Efficient Document Retrieval

Chiranjeevi Bura

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AI and Machine Learning Approaches for Efficient Document Retrieval

Chiranjeevi Bura

Subject area: Science,Engineering and Technology · Area of research: AI and Machine Learning

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

How to cite this paper

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

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

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

@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},
  }