AI and Machine Learning Approaches for Efficient Document Retrieval

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@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},
  }