命名实体消歧研究进展综述  

Developments of Named Entity Disambiguation

作　　者：温萍梅 叶志炜 丁文健 刘颖[2] 徐健[1] Wen Pingmei;Ye Zhiwei;Ding Wenjian;Liu Ying;Xu Jian(School of Information Management,Sun Yat-Sen University,Guangzhou 510006,China;Sun Yat-Sen University Library,Guangzhou 510275,China)

机构地区：[1]中山大学资讯管理学院,广州510006 [2]中山大学图书馆,广州510275

出　　处：《数据分析与知识发现》2020年第9期15-25,共11页Data Analysis and Knowledge Discovery

基　　金：广东省自然科学基金项目“情感分歧度量化模型及其应用研究”(项目编号:2018A030313981)的研究成果之一。

摘　　要：【目的】调研近年来命名实体消歧领域的相关研究和资源,重点介绍命名实体消歧方法研究进展。【文献范围】使用知网数据库、万方数据知识服务平台和EBSCO外文期刊平台检索命名实体消歧相关文献,共选择57篇代表性文献和电子资源。【方法】从实体显著性、上下文相似度、实体关联度、深度学习和特殊标识资源5个角度对命名实体消歧的方法和思路进行归纳总结,并对可用的辅助知识库和开源工具以及国际评测会议进行梳理。【结果】传统的方法经典易用,而近年来出现的深度学习等新方法,则明显地提升了消歧效果。有效的消歧模型往往整合了不同类型方法,以期达到最优消歧效果。【局限】基于已有文献对各种方法的对比分析尚存在一定的主观性。【结论】现有的命名实体消歧方法仍然处在发展阶段,未来可利用人工智能方法和领域资源进一步提升实体消歧效果。[Objective]This paper reviews research and resources in the field of named entity disambiguation(NED)with a focus on the NED methods.[Coverage]We retrieved 57 representative papers and electronic resources from CNKI,Wanfang Data Knowledge Service Platform,and EBSCO.[Methods]First,we summarized the NED principles and methods from the perspectives of entity prominence,context similarity,entity relationship,deep learning and special identification resources.Then,we explored useful knowledge bases,open source tools as well as international conferences on NED evaluation.[Results]Traditional and classic methods were easy to use,while the new ones(e.g.,deep learning)significantly improved the results of NED.Effective models often integrated various methods to yield the optimal results.[Limitations]There are subjectivity factors in comparing different methods from the literature.[Conclusions]The NED methods are still developing and could be further improved by artificial intelligence and domain resources.

关 键 词：命名实体消歧 知识库 实体链接 聚类 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] G250[自动化与计算机技术—计算机科学与技术]