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| Format: | Recurso digital |
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Zenodo
2023
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| Online Access: | https://doi.org/10.5281/zenodo.15128372 |
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| _version_ | 1866902236306604032 |
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| author | IRE Journals |
| author_facet | IRE Journals |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15128372 |
| institution | Zenodo |
| language | |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI and Machine Learning Approaches for Efficient Document Retrieval IRE Journals <p>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.</p> |
| title | AI and Machine Learning Approaches for Efficient Document Retrieval |
| url | https://doi.org/10.5281/zenodo.15128372 |