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MoDora: Tree-Based Semi-Structured Document Analysis System

Summary: MoDora transforms OCR fragments into layout-aware components and a Component-Correlation Tree that preserves hierarchy, spatial distinctions, and cross-region links. Question-type-aware retrieval combines grid-based location search with LLM-guided semantic pruning, improving QA accuracy by 5.97–61.07%. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7462
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,270 | 29.54%
DOI
10.1145/3802089

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Authors

BibTeX Citation

@inproceedings{xu_sigmod26,
        title = {{MoDora: Tree-Based Semi-Structured Document Analysis System}},
        author = {Xu, Bangrui and Yao, Qihang and Tang, Zirui and Zhou, Xuanhe and He, Yeye and Yu, Shihan and Xu, Qianqian and Wang, Bin and Li, Guoliang and He, Conghui and Wu, Fan},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802089},
        url = {https://dl.acm.org/doi/10.1145/3802089},
        year = {2026}
}

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