We consider the problem of jointly training structured models for extraction from sources whose instances enjoy partial overlap. This has important applications like user-driven ad-hoc information extraction on the web. Such applications present new challenges in terms of the number of sources and their arbitrary pattern of overlap not seen by earlier collective training schemes applied on two sources. We present an agreement-based learning framework and alternatives within it to trade-off tractability, robustness to noise, and extent of agreement. We provide a principled scheme to discover low-noise agreement sets in unlabeled data across the sources. Through extensive experiments over 58 real datasets, we establish that our method of additively rewarding agreement over maximal segments of text provides the best trade-offs, and also scores over alternatives such as collective inference, staged training, and multi-view learning.
MathDoc: Benchmarking Structured Extraction and Active Refusal on Noisy Mathematics Exam Papers
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Gaotu Techedu Inc.（高途科技公司）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Science and Technology of China（中国科学技术大学）
Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health
Quantitative Biomedical Research Center, Department of Health Data Science and Biostatistics, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center（定量生物医学研究中心，健康数据科学与生物统计学系，彼得·奥·多尼尔公共卫生学院，德克萨斯西南医学中心）
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Computer Science, University of California, Los Angeles（加州大学洛杉矶分校计算机科学系）
RadEx: A Framework for Structured Information Extraction from Radiology Reports based on Large Language Models
Institute for Patient-centered Digital Health, Bern University of Applied Sciences, Biel, Switzerland（以患者为中心的数字健康研究所，伯恩应用科学大学，比尔，瑞士）；ID Suisse AG, St. Gallen, Switzerland（ID瑞士股份有限公司，圣加尔，瑞士）
Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院（SIMTech），新加坡科技研究局（A*STAR））；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（先进再制造与技术中心（ARTC），新加坡科技研究局（A*STAR））；Nanyang Technological University（南洋理工大学）
A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction
Research Institute of Mine Artificial Intelligence in Chinese Institute of Coal Science（中国煤炭学会矿山人工智能研究所）；Tiandi Science and Technology Co., Ltd.（天地科技股份有限公司）；College of Software, Northeastern University（东北大学软件学院）；Institute of Intelligent Manufacturing, Nanjing Tech University（南京工业大学智能制造研究院）；School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University（上海第二工业大学智能制造与控制工程学院）；Faculty of Engineering, University of Toyama（富山大学工学部）；Foshan Graduate School of Innovation, Northeastern University（东北大学佛山创新研究生院）
STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA（麦戈文脑科学研究所，麻省理工学院，马萨诸塞州剑桥市）；Fylo Labs Inc., New York, NY, USA（Fylo实验室公司，纽约州纽约市）；Allen Institute for Brain Science, Seattle, WA, USA（艾伦脑科学研究所，华盛顿州西雅图市）
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China（南京大学新型软件技术国家重点实验室）；Amap, Alibaba Group, China（阿里巴巴集团阿地图）；University of Alberta, Edmonton, Canada（阿尔伯塔大学）；The University of Tokyo, Tokyo, Japan（东京大学）
From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control
Quantitative and Digital Law Laboratory（量化与数字法律实验室）；CIRSFID - Alma AI, Faculty of Law University of Bologna（CIRSFID - 阿尔玛AI，博洛尼亚大学法学院）；Robert Schuman Centre European University Institute（罗伯特·施曼中心欧洲大学研究所）
Leveraging Prompt-Learning for Structured Information Extraction from Crohn's Disease Radiology Reports in a Low-Resource Language
Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology（以色列理工学院数据与决策科学学院）；Shaare Zedek Medical Center（沙雷泽德克医疗中心）；Faculty of Biomedical Engineering, Technion - Israel Institute of Technology（以色列理工学院生物医学工程学院）；Hospital for Sick Children（病童医院）
提出SMP-BERT提示学习方法，利用报告结构从希伯来语克罗恩病放射学报告中提取结构化信息，显著优于传统微调，尤其在罕见病症检测上（AUC 0.99 vs 0.94，F1 0.84 vs 0.34），推动低资源语言的AI诊断。
Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Maastricht University（马斯特里赫特大学）；Semanticly Ltd（Semanticly有限公司）；Robert Bosch LLC（罗伯特·博世有限责任公司）；University of Colorado（科罗拉多大学）；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）
