In modern electronic medical records (EMR) much of the clinically important data - signs and symptoms, symptom severity, disease status, etc. - are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge.
School of Medical Engineering, Xinxiang Medical University（新乡医学院医学工程学院）；Shanghai AI Laboratory（上海人工智能实验室）；Amazon（亚马逊公司）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School, Harvard University（哈佛大学医学院）；Toursun Synbio（拓臻生物）；Monash University（莫纳什大学）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；University of Cambridge（剑桥大学）
LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine
Beijing Jiaotong University（北京交通大学）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；Hubei University of Chinese Medicine（湖北中医药大学）；Hubei Province Academy of Traditional Chinese Medicine（湖北省中医药研究院）；China Academy of Chinese Medical Sciences（中国中医科学院）；Xiyuan Hospital（西苑医院）；National Clinical Research Center for Chinese Medicine Cardiology（国家中医心血管病临床医学研究中心）；Hubei Provincial Clinical Research Center for Acupuncture and Moxibustion in Obesity Treatment（湖北省肥胖病针灸临床研究中心）；Hubei Shizhen Laboratory（湖北时珍实验室）；Tianjin Ta（天津（此处原文未完整，按原文提取））
Who's Laughing Now? An Overview of Computational Humour Generation and Explanation
Department of Computer Science, The University of Sheffield, UK（谢菲尔德大学计算机科学系）；Department of Computer Science, The University of Manchester, UK（曼彻斯特大学计算机科学系）
Overview of the SV-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications
University of Mannheim（曼海姆大学）；GESIS – Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；Fraunhofer ISI（弗劳恩霍夫系统与创新研究所）；Stuttgart Media University（斯图加特传媒大学）
Graph Neural Networks for Natural Language Processing: A Survey
Pinterest（拼趣）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Zhejiang University（浙江大学）；IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Central China Normal University（华中师范大学）；Nanjing University（南京大学）；Simon Fraser University（西蒙弗雷泽大学）
