CHIP2020评测任务1概述:中文医学文本命名实体识别  

Overview of CHIP2020 Shared Task 1:Named Entity Recognition in Chinese Medical Text

作　　者：李雯昕 张坤丽[1,4] 关同峰 张欢 朱田恬[3] 常宝宝 陈清财 LI Wenxin;ZHANG Kunli;GUAN Tongfeng;ZHANG Huan;ZHU Tiantian;CHANG Baobao;CHEN Qingcai(School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou,Henan 450001,China;Key Laboratory of Computational Linguistics,Ministry of Education,Peking University,Beijing 100871,China;School of Computer Science and Technology,Harbin Institute of Technology(Shenzhen),Shenzhen,Guangdong 518055,China;Peng Cheng Laboratory Artificial Intelligence Research Center,Shenzhen,Guangdong 518055,China)

机构地区：[1]郑州大学计算机与人工智能学院,河南郑州450001 [2]北京大学计算语言学教育部重点实验室,北京100871 [3]哈尔滨工业大学(深圳)计算机科学与技术学院,广东深圳518055 [4]鹏城实验室人工智能研究中心,广东深圳518055

出　　处：《中文信息学报》2022年第4期66-72,共7页Journal of Chinese Information Processing

基　　金：河南省医学科技攻关计划省部共建项目(SB201901021);郑州市协同创新重大专项科技攻关项目(20XTZX1120);河南省高等学校重点科研项目(20A520038)。

摘　　要：第六届中国健康信息处理会议(China Conference on Health Information Processing,CHIP2020)组织了中文医疗信息处理方面的6个评测任务,其中任务1为中文医学文本命名实体识别任务,该任务的主要目标是自动识别医学文本中的医学命名实体。共有253支队伍报名参加评测,最终37支队伍提交了80组结果,该评测以微平均F_(1)值作为最终评估标准,提交结果中最高值达68.35%。The 6th China Conference on Health Information Processing(CHIP2020)organized six evaluation tasks in Chinese medical information processing,among which task 1 was named entity recognition task of Chinese medical text.The main purpose of this task is to automatically identify medical named entities in medical texts.A total of 253 teams signed up for the evaluation,and 37 teams finally submitted 80 sets of results.The micro-average F_(1) is used as the final evaluation criteria,and the highest value of the submitted results reached 68.35%.

关 键 词：命名实体识别 医学文本 自然语言处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]