인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.8
- 수록면
- 1,001 - 1,014 (14page)
- DOI
- 10.9717/kmms.2025.28.8.1001
이용수
초록· 키워드
This paper presents a novel field-adaptive methodology for dense retrieval of structured documents, tackling the persistent semantic gap between natural language queries and field-based content organization. As structured document repositories proliferate in enterprise environments, traditional dense retrieval methods face challenges due to the heterogeneous composition of fields and uneven semantic density. Our approach introduces three key innovations. First, we employ fine-tuned language models with similarity filtering to generate high-fidelity training data, addressing the scarcity of reliable query-document pairs. Second, we implement query-length-based adaptive field weighting, dynamically adjusting the contribution of titles, descriptions, and metadata during bi-encoder contrastive training. Third, we design a two-stage hybrid ranking strategy that combines the efficiency of bi-encoders with the precision of cross-encoders through optimized score integration. Extensive experiments on the Crello dataset, comprising over 25,000 structured documents, demonstrate a 33.8% improvement in Mean Reciprocal Rank (MRR) compared to the baseline, while maintaining inference efficiency. These results establish a scalable and domain-independent solution for structured document retrieval, offering both theoretical contributions and practical feasibility for real-world deployment.
#Document Dense Retrieval
#Structured Documents
#Field-Adaptive Embedding
#Hybrid Ranking
#Contrastive Learning
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목차
- ABSTRACT
- 1. INTRODUCTION
- 2. BACKGROUNDS AND RELATED WORKS
- 3. PROPOSED APPROACH
- 4. Experiments and Performance Analysis
- 5. CONCLUSION
- REFERENCE
참고문헌
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UCI(KEPA) : I410-151-25-02-094042564
