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References (17)
Founder's Pitch
""MoDora optimizes semi-structured document analysis for accurate question answering by organizing document elements in a hierarchical structure.""
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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Series A Potential
2/4 signals
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Why It Matters
This research addresses the challenge of achieving precise question answering over semi-structured documents, which are prevalent and critical in domains such as legal, financial, and scientific reporting. Without efficient document analysis systems like MoDora, extracting actionable insights from these documents would remain a laborious task.
Product Angle
Transform MoDora into a plugin or API service that integrates with existing document management systems, focusing on enabling structured analysis and informative retrieval for businesses handling large volumes of semi-structured documents.
Disruption
MoDora could replace traditional manual document analysis methods and improve on existing OCR-based document handling and rudimentary keyword search systems by offering more contextually aware and relationally precise outputs.
Product Opportunity
The opportunity is substantial considering the growth of digital documents in enterprises seeking efficient document management solutions. These solutions serve legal, financial, and research sectors, with primary payers being companies within these industries.
Use Case Idea
Develop a SaaS platform for legal firms that uses MoDora to analyze and extract insights from lengthy case documents, enabling lawyers to quickly answer client-specific queries.
Science
MoDora employs a Local-Alignment Aggregation strategy to reassemble OCR-extracted elements into coherent components. It constructs a Component-Correlation Tree (CCTree) to model inter-component and layout relations. Additionally, it uses a question-type-aware retrieval method, combining location-based and semantic-based tactics, to manage evidence aggregation and verification in semi-structured documents.
Method & Eval
MoDora was tested through a series of benchmarks comparing its accuracy against baseline methods, showing an improvement range between 5.97% to 61.07%. This suggests significant effectiveness in handling the complexities of semi-structured document layouts and formats over existing solutions.
Caveats
Challenges include adapting to varied document formats beyond those considered, the potential need for complex setup in diverse document environments, and limitations if document layout significantly changes after digitization. Dependence on OCR accuracy remains a critical limiting gate.
Author Intelligence
Bangrui Xu
Shanghai Jiao Tong University
dreameternal@sjtu.edu.cn
Qihang Yao
Shanghai Jiao Tong University
yaoqihang@sjtu.edu.cn
Zirui Tang
Shanghai Jiao Tong University
tangzirui@sjtu.edu.cn
Xuanhe Zhou
Shanghai Jiao Tong University
zhouxuanhe@sjtu.edu.cn
Yeye He
Microsoft Research
yeyehe@microsoft.com
Shihan Yu
Beihang University
yushihan070611@qq.com
Qianqian Xu
Beihang University
24421025@buaa.edu.cn
Bin Wang
Shanghai AI Lab
wangbin@pjlab.org.cn
Guoliang Li
Tsinghua University
liguoliang@tsinghua.edu.cn
Conghui He
Shanghai AI Lab
heconghui@pjlab.org.cn
Fan Wu
Shanghai Jiao Tong University
fwu@cs.sjtu.edu.cn