Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. With both the internal and external knowledge of Chinese characters, our framework can better capture the semantic information of Chinese characters. To demonstrate the effectiveness of the proposed framework, we conduct extensive experiments on three real-world datasets in distinct domains. Experimental results show consistent and significant superiority and robustness of our proposed framework. Our code and dataset will be released at: https://gitee.com/tmg-nudt/multi-view-of-expert-for-chineserelation-extraction
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases
Tsinghua University（清华大学）；Peking University（北京大学）；University of Glasgow（格拉斯哥大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Queen Mary University of London（伦敦玛丽女王大学）；Northeastern University（东北大学）
A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text
HwaMei Hospital, University of Chinese Academy of Sciences（中国科学院大学华美医院）；Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences（中国科学院大学宁波生命与健康产业研究院）
A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis
School of Biomedical Engineering, Capital Medical University（首都医科大学生物医学工程学院）；Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Tsinghua University（清华大学）
CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Henan University（河南大学）
The Joint Entity-Relation Extraction Model Based on Span and Interactive Fusion Representation for Chinese Medical Texts with Complex Semantics
School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Cooperative Innovation Center of Internet Healthcare, Zhengzhou University（郑州大学互联网医疗协同创新中心）；Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家心血管病中心阜外医院）；Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院老年医学科）
Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPT
University of Amsterdam（阿姆斯特丹大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Simon Fraser University（西蒙菲莎大学）；Northeastern University（东北大学）
Research on Information Extraction of LCSTS Dataset Based on an Improved BERTSum-LSTM Model
College of Information Science and Technology, Donghua University（东华大学信息科学与技术学院）；College of Physics, Donghua University（东华大学物理学院）；Glorious Sun School of Business & Management, Donghua University（东华大学旭日工商管理学院）；College of Medical Technology, Shanghai University of Medicine & Health Sciences（上海健康医学院医学技术学院）；College of Information Sciences and Technology, Donghua University（东华大学信息科学与技术学院）
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer Science, Nankai University（南开大学计算机学院）；School of Engineering, Westlake University（西湖大学工学院）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖高等研究院前沿技术研究所）
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Peking University（北京大学）
Web 2.0带来了大量用户产生的数据，这些数据揭示了一个人的思想、经验和知识，这些数据是信息抽取和知识库构建等许多任务的重要来源。然而，文本的口语化特征对当前更适应语言形式的自然语言处理技术提出了新的挑战。省略是一种常见的语言现象，特别是在口语中，当人们从语境中理解某些词时，省略了它们，这阻碍了依存句法分析的提高，而依存句法分析对于依赖句子意义的任务具有重要意义。为了促进这一领域的研究，我们发布了一个包含319个微博的中文依存关系树库，其中包含572个句子，并保留了遗漏和上下文。
Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations
School of Computer Science, Peking University（北京大学计算机科学学院）；Institute of Computational Social Science, Peking University (Qingdao)（北京大学计算社会科学研究所）；College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；The University of Sheffield（谢菲尔德大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets
Chengdu Institute of Computer Application, Chinese Academy of Sciences（中国科学院成都计算机应用研究所）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Public Big Data, Guizhou University（贵州大学公共大数据国家重点实验室）
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Ricoh Software Research Center (Beijing)（理光软件研究所（北京））；School of Software, Harbin University of Science and Technology（哈尔滨理工大学软件学院）；Medical Records Room, the Second Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第二医院病案室）
Recognizing Chinese Judicial Named Entity using BiLSTM-CRF
School of Computer Science, Sichuan University（四川大学计算机学院）；School of electronic information, Sichuan University（四川大学电子信息学院）；Law School, Sichuan University（四川大学法学院）
Advancing Chinese biomedical text mining with community challenges
Sichuan University（四川大学）；West China Hospital（华西医院）；Tongji University（同济大学）；Shanghai Business School（上海商学院）；Takeda Co. Ltd.（武田制药有限公司）；Harbin Institute of Technology（哈尔滨工业大学）
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）
