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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Hybrid Embedding Retrieval Augmented Document Analysis - A Review |
| Authors Name: | Dr Vinod Kumar P , Amrutha M , Keerthana R M , Srushti A T , Yashaswini S Raj |
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IJRTI_205891
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| Published Paper Id: | IJRTI2508143 |
| Published In: | Volume 10 Issue 8, August-2025 |
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| Abstract: | The exponential growth of documents, case law, and statutory materials has intensified the need for intelligent systems capable of efficient and accurate text analysis. This review paper explores the integration of hybrid embedding techniques—merging sparse (e.g., TF-IDF, BM25) and dense (e.g., BERT-based) representations—within retrieval-augmented frameworks for document analysis. We provide a comprehensive overview of current methodologies, compare various embedding strategies, and assess their impact on tasks such as question answering, precedent retrieval, and contract analysis. The paper also discusses the challenges of domain-specific training, legal terminology disambiguation, and data privacy, and outlines future directions for building robust, scalable, and interpretable hybrid systems in legal AI. Moreover, the paper evaluates the performance trade-offs in hybrid models concerning retrieval latency, interpretability, and compliance with legal standards. Special attention is given to the role of prompt engineering in retrieval-augmented models and the implications of using generative models like GPT or LLaMA within regulated environments. Finally, we highlight emerging trends, including multimodal reasoning, cross-lingual embeddings for comparative law, and the integration of ontologies with neural retrieval systems to improve explainability and trustworthiness. Through this review, we aim to guide future research and system design in AI, emphasizing the promise of hybrid embedding strategies for transforming document analysis. |
| Keywords: | Hybrid Embeddings, Retrieval-Augmented Generation, Legal NLP, Sparse and Dense Retrieval, Semantic Search, Document Analysis, High-Precision AI, Information Retrieval. |
| Cite Article: | "Hybrid Embedding Retrieval Augmented Document Analysis - A Review", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.b322-b326, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508143.pdf |
| Downloads: | 000341 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2508143
Registration ID:205891
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier):
Page No: b322-b326 Country: Mysuru, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508143 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508143 |
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ISSN: 2456-3315
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