Universal Information Extraction as Unified Semantic Matching
Baidu Inc.（百度公司）；Chinese Information Processing Laboratory（中文信息处理实验室）；State Key Laboratory of Computer Science（计算机科学国家重点实验室）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
Jie Lou, Yaojie Lu, Dai Dai, Wei Jia, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu
The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures. Traditional methods require task-specific model design and rely heavily on expensive supervision, making them difficult to generalize to new schemas. In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas. Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing. In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand. Empirical evaluation on 4 IE tasks shows that the proposed method achieves state-of-the-art performance under the supervised experiments and shows strong generalization ability in zero/few-shot transfer settings.
Unified Structure Generation for Universal Information Extraction
University of Chinese Academy of Sciences（中国科学院大学）；Baidu Inc.（百度公司）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE
Faculty of Informatics, TU Wien（信息学院，维也纳技术大学）；D!ARC, University of Klagenfurt（D!ARC，克雷格福特大学）；Digital Philology, University of Vienna（数字人文，维也纳大学）；Khoury College of Computer Sciences, Northeastern University（科赫计算机科学学院，东北大学）
Pre-training to Match for Unified Low-shot Relation Extraction
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
INESC TEC；FCUP（波尔图大学理学院）；ICMC-USP（圣保罗大学圣卡洛斯数学与计算机科学研究所）；University of São Paulo（圣保罗大学）；FEP（波尔图大学经济学院）
本文论述了实体提取任务（命名实体识别）的文本挖掘过程，旨在揭示非平凡的语义结构，如实体或社区之间的关系和相互作用。本文提出了一种简单有效的命名实体抽取算法。该方法采用灵活的模式匹配、词性标注和基于词典的规则，称为PAMPO（Pattern Matching and PO s tagingbased algorithm for NER）。它被开发用于处理用葡萄牙语编写的文本，但它也可能适用于其他语言。我们比较我们的方法与目前的替代品，支持命名实体识别（NER）的内容写在葡萄牙语。这些是炼金术师泽曼塔和伦勃朗在葡萄牙语写的几个文本上的实体提取方法的疗效评价表明，$recall$和$F_1$措施有相当大的改善。
Universal Information Extraction with Meta-Pretrained Self-Retrieval
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Xiaomi AI Lab, Xiaomi Inc.（小米公司小米人工智能实验室）
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Geo-Information Engineering（地理信息工程国家重点实验室）；Xi'an Research Institute of Surveying and Mapping（西安测绘研究所）
Applying Relation Extraction and Graph Matching to Answering Multiple Choice Questions
Graduate School of Informatics, Kyoto University（京都大学信息科学研究生院）；Center for Innovative Research and Education in Data Science, Kyoto University（京都大学数据科学创新研究与教育中心）
Joint Entity and Relation Extraction with Set Prediction Networks
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Unisound Information Technology Co., Ltd（北京云知声信息技术有限公司）
Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease
Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology（1 数据与决策科学学院，技术离子-以色列理工学院）；Faculty of Biomedical Engineering, Technion – Israel Institute of Technology（2 生物医学工程学院，技术离子-以色列理工学院）；Juliet Keidan Institute of Pediatric Gastroenterology, Hepatology and Nutrition, The Eisenberg R&D Authority, Shaare Zedek Medical Center, The Hebrew University School of Medicine（3 肝胆营养研究所，Eisenberg R&D机构，Shaare Zedek医疗中心，希伯来大学医学院）；Pediatric Radiology Unit, Radiology Department, The Eisenberg R&D Authority, Shaare Zedek Medical Center, The Hebrew University of Jerusalem（4 儿童放射科单位，放射科部门，Eisenberg R&D机构，Shaare Zedek医疗中心，希伯来大学杰里科分校）
Retrieval-Augmented Code Generation for Universal Information Extraction
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所网络数据科学与技术重点实验室）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Platform and Content Group, Tencent（腾讯平台与内容事业群）
