命名实体识别任务综述  

Overview of Named Entity Recognition Tasks

作　　者：高翔 王石[2] 朱俊武[1] 梁明轩[1,2] 李阳 焦志翔 GAO Xiang;WANG Shi;ZHU Junwu;LIANG Mingxuan;LI Yang;JIAO Zhixiang(College of Information Engineering,Yangzhou University,Yangzhou,Jiangsu 225000,China;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]扬州大学信息工程学院,江苏扬州225000 [2]中国科学院计算技术研究所,北京100190

出　　处：《计算机科学》2023年第S01期16-23,共8页Computer Science

基　　金：国家自然科学基金(61702234);国家242信息安全计划项目(2021A008);北京市科技新星计划交叉学科合作课题(Z191100001119014);国家重点研发计划重点专项(2017YFC1700300,2017YFB1002300)。

摘　　要：命名实体识别作为自然语言处理中一项十分基础的任务,为其他许多下游任务的高效完成奠定了基础。其目的是从一段用自然语言描述的文本中识别出相应的实体并标注其类型,以此为其他相关任务作出数据标注的准备。首先介绍了命名实体识别任务的发展历程以及在对应背景下相关研究用到的重点方法,包括自诞生初期用到的基于规则和字典的方法以及后期发展衍生出的基于统计学、深度学习的方法。其次总结了一些该领域比较主流的研究方向,包括低资源条件下的命名实体识别、嵌套命名实体识别以及跨语言的命名实体识别等,这些方向都是近期该任务的热门研究趋势,包含了该任务目前最为流行的研究方法。最后总结了研究中的相关经验,展望了该任务未来的发展方向及难点。Named entity recognition,as a very basic task in natural language processing,lays the foundation for the efficient completion of many other downstream tasks.Its purpose is to identify the corresponding entity from a text described in natural language and label its type,so as to make preparations for data labeling for other related tasks.This paper first introduces the deve-lopment process of named entity recognition tasks and the key methods used in related research in the corresponding context,including the rule-based and dictionary-based methods used in the early days of the birth,and the statistics and deep learning derived from the later development.Secondly,it summarizes some of the more mainstream research directions in this field,including named entity recognition under low-resource conditions,nested named entity recognition,and cross-language named entity recognition.These directions are the hot research trends of this task recently,including the most popular research method of this task at present.Finally,the relevant experience in the research is summarized,and the future development direction and difficulties of the task are prospected.

关 键 词：命名实体识别 嵌套命名实体识别 深度学习 低资源 跨语言 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]