Many NLP models gain performance by having access to a knowledge base. A lot of research has been devoted to devising and improving the way the knowledge base is accessed and incorporated into the model, resulting in a number of mechanisms and pipelines. Despite the diversity of proposed mechanisms, there are patterns in the designs of such systems. In this paper, we systematically describe the typology of artefacts (items retrieved from a knowledge base), retrieval mechanisms and the way these artefacts are fused into the model. This further allows us to uncover combinations of design decisions that had not yet been tried. Most of the focus is given to language models, though we also show how question answering, fact-checking and knowledgable dialogue models fit into this system as well. Having an abstract model which can describe the architecture of specific models also helps with transferring these architectures between multiple NLP tasks.
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）；McGovern Medical School, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦戈文医学院）
Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Engineering, Westlake University（工程学院，西湖大学）
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；Cardiff University（卡迪夫大学）；Queen Mary University of London（伦敦女王学院）
Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard University（哈佛大学）；George Mason University（乔治梅森大学）
Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia
Leiden University Medical Centre（莱顿大学医学中心）；Lancaster University（兰卡斯特大学）；University of Manchester（曼彻斯特大学）；The Leiden Institute of Advanced Computer Science（莱顿先进计算机科学研究所）
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing
Imperial College London（伦敦帝国理工学院）；Northeastern University（东北大学）；Data Science for Social Impact, University of Pretoria（比勒陀利亚大学数据科学为社会影响）；Kaduna State University（卡杜纳州大学）；HausaNLP；Bayero University, Kano（卡诺巴耶罗大学）；Universiti Teknologi PETRONAS（PETRONAS科技大学）；University of the Witwatersrand（沃斯堡大学）；Federal University of Lafia（拉法联邦大学）
Deep Neural Network Based Relation Extraction: An Overview
Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province（四川省可信云计算与大数据重点实验室）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）
知识是一种形式化的认识世界的方式，为下一代人工智能提供了人类水平的认知和智能。知识的表示之一是实体之间的语义关系。关系抽取（RelationExtraction，RE）是信息抽取的一个子任务，是自动获取这些重要知识的有效途径，在自然语言处理（Natural Language Processing，NLP）中起着至关重要的作用。其目的是从自然语言文本中识别实体之间的语义关系。到目前为止，有几个研究RE在以前的作品，其中记录了这些技术的基础上，深度神经网络（DNN）成为一个流行的技术在这项研究中。特别是基于DNN的监督和远程监督方法是RE最流行和最可靠的解决方案。本文1）介绍了一些基本概念，2）从两个角度对分布式神经网络在RE中的应用进行了全面的概述：监督RE，它试图改进标准RE系统;远程监督RE，它采用分布式神经网络设计句子编码器和去噪方法。我们进一步3）涵盖了一些新的方法和最近的趋势，以及讨论可能的未来研究方向，这项任务。
ChiMDQA: Towards Comprehensive Chinese Document QA with Fine-grained Evaluation
Beijing Jiaotong University（北京交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing University of Technology（北京理工大学）；Foxit Software Co. Ltd（Foxit软件有限公司）
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）
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）
A Systematic Investigation of Commonsense Knowledge in Large Language Models
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；DeepMind（深度思维）；Inflection AI（Inflection人工智能公司）；Reka（Reka公司）；Cohere（Cohere公司）；University of Oxford（牛津大学）
Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations
University of Technology Sydney（技术大学悉尼）；Xi’an Jiaotong-Liverpool University（西安交通大学-利物浦大学）；University of Utah（犹他大学）；Harvard University（哈佛大学）；The University of Melbourne（墨尔本大学）；The University of Manchester（曼彻斯特大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）
