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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (4): 1-15    DOI: 10.11925/infotech.2096-3467.2021.1000
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Research Progress on Citation Analysis of Scientific Papers
Wang Lu,Le Xiaoqiu()
National Science Library, Chinese Academy of Sciences, Beijing 100190, China;Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper reviews the research progress of citation content analysis in recent years and clarifies the research direction and technology development trend. [Coverage] HowNet, Scopus, Semantic Scholar, and other search platforms are used to search papers with keywords such as “citation full text”, “citation context”, “citation content” and so on, and manual screening is conducted. [Methods] Research on citation analysis is summarized and compared from four aspects: discrimination of relevant concepts, main research directions, key technologies, analysis tools and platforms, and existing problems and future research directions are raised. [Results] New ideas and methods are emerging in citation content analysis research directions such as citation motivation, citation evaluation, knowledge flow, and paper recommendation. Key common technologies for citation content analysis have achieved much progress in citation extraction, citation location identification, citation sentiment analysis, and knowledge point identification. [Limitations] It mainly summarizes and analyzes the relevant research from the macro level and does not elaborate on the content in all aspects in-depth. [Conclusions] Citation content analysis has unique advantages over citation analysis. With the rapid iteration of natural language processing technology, it will have a broad development prospect.

Key wordsCitation Context      Citation Context Analysis      Machine Learning      Deep Learning     
Received: 06 September 2021      Published: 12 May 2022
ZTFLH:  G35  
Corresponding Authors: Le Xiaoqiu,ORCID:0000-0002-7114-5544     E-mail: lexq@mail.las.ac.cn

Cite this article:

Wang Lu, Le Xiaoqiu. Research Progress on Citation Analysis of Scientific Papers. Data Analysis and Knowledge Discovery, 2022, 6(4): 1-15.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1000     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I4/1

作者 概念 内涵
Small[1] 引文语境 施引文献文本中提及参考文献的段落或文本片段,即相应引用标记周围的文本
引文语境分析 对引文语境进行分析,判断引用某篇论文的原因或动机,以及表征被引文献的研究特征
祝青松等[2] 引文上下文 引用句在内容上相关联的上下文句子
引文内容分析 对引用句或引文上下文的内容进行分析,更加注重引文之间在内容上的语义关联,揭示被引文献对施引文献的影响
Ding等[3] 基于内容的引文分析 通过分析引文上下文的句法和语义信息判断引文价值。其中句法信息指引用位置、引用风格等,语义信息指如何引用参考文献、知识概念或领域实体等
赵蓉英等[4] 全文本引文分析 通过自然语言处理、文本挖掘、情感分析以及可视化等方法技术,对引文的引用情况和引用动机等进行挖掘、分析和展示,包括结构和语义两个分析层次
胡志刚[5] 全文引文分析 对学术论文正文中出现的引用信息和引用行为进行研究的分析方法,包括引用位置分析、引用强度分析和引用语境分析三个维度
刘盛博等[6] 引用内容 能够表征施引文献引用参考文献的文本内容,通常用一个或多个句子表达,包含量和质两方面的信息
引用内容分析 从施引文献的全文入手,并聚焦于引用片段,对科学引证过程中,具有明确引用标识的知识传播内容(引用内容)的位置分布和内容主题进行客观、系统、定量的分析
刘浏等[8] 引用内容 对引文的文本描述,通常是引用位置附近一定范围内的文本,表明施引文献与被引文献之间的引用关系
引用内容分析 基于引用内容的引文分析,关注引用内容本身,需要对文本进行语义挖掘,涉及自然语言处理、机器学习等研究领域
Related Concepts of Citation Content Analysis
对比项 引文分析 引用内容分析
数据对象 论文元数据 全文
数据粒度 篇级 句子级/篇章级
分析侧重点 统计指标 文本内容挖掘
主体方法 统计模型 文本语义分析、篇章分析
Comparison of Citation Analysis and Citation Content Analysis
作者 发表时间 引用动机分类
Garfield[13] 1964 1.致敬;2.识别参考文献方法、工具;3.背景;4.纠正;5.批评过去研究;6.证明观点;7.警告后续研究;8. 推广成果;9.证明数据和事实类别;10.识别原始文献;11.否认他人成果或观点;12.讨论研究优先权
Spiegel-
Rosing[14]
1977 1.背景;2.与本研究背离;3.参考概念、定义或解释;4.使用研究数据;5.比较;6.证明观点或假设;7.正向;8.负向;9.证明参考文献的数据或解释;10.推翻参考文献数据或假设;11.推翻或质疑参考文献数据或假设;12.对参考文献数据或假设提出新的解释
Peritz[15] 1983 1.基础;2.背景;3.方法;4.比较;5.论证;6.文件记录;7.历史引文;8.随意引文
Garzone[16] 1997 1.负面引用(7小类);2.肯定引用(5小类);3.假设引用(4小类);4.实验性引用;5.方法性引用(5小类);6.解释或发展(3小类);7.未来研究(2小类);8.概念引用(2小类);9.对比(2小类);10.提醒(5小类)
Nanba等[17] 2000 1.基于;2.比较;3.其他
Pham等[18] 2003 1.基础;2.支持;3.局限;4.比较
Teufel等[12] 2006 1.指出参考文献的不足;2.比较;3.同意或使用参考文献;4.中性
Dong等[19] 2011 1.背景;2.基本思想;3.技术基础;4.比较
Hernandez-
Alvarez等[20]
2017 1.使用;2.比较;3.批判;4.背景
Le等[21] 2019 1.正向引用;2.中性引用;3.负向引用
彭泽等[22] 2020 1.肯定性引用;2.肯定性继承;3.批判性引用;4.批判性继承
Researches on Citation Motivation Classification
Schematic Diagram of Knowledge Flow Between Nodes
Work Flow of Citation Analysis
Schematic Diagram of Explicit and Implicit Citation Sentence
Schematic Diagram of Citation Sentence Fragments
方法 作者 技术 特征 数据集 结果(F/%)
监督学习方法 Angrosh等[64] CRF 引文特征,句子特征(术语) LNCS中1 063条引文上下文 74.3
Sondhi等[65] HMM ACL中15 000条引文上下文 49.5
雷声伟等[66] SVM, CRF N-Grams,词性特征 ACL中130篇文献 85.6
无监督学习方法 Jebari等[54] LDA, Sentence2Vec, Doc2Vec
金贤日等[67] Word2Vec, TFIDF, VSM 多义词语料库 ACL中207条引文上下文 92.6
Related Works of Implicit Citation Sentence Extraction
方法 作者 技术 特征 数据集 结果(F/%)
传统机器学习方法 陆伟等[74] CRF 自定义词表,序列特征 JASIST中300篇文献 90.0
Tuarob等[80] RF, SVM, RIPPER, NB 模式特征,样式特征,结构特征 CiteSeer中217篇文献 92.4
王东波等[75] CRF, Bi-LSTM, SVM 章节句子数目特征,章节内词汇特征,章节标题高频词特征 JASIST中500篇文献 92.9
深度学习方法 王佳敏等[76] CNN,LSTM, 投票方法 ScienceDirect中4 000篇文献 86.0
秦成磊等[73] BERT,层级注意力 PLoS系列4种期刊中共22 114篇文献 97.9
王倩等[77] CNN,RNN ScienceDirect中26万篇文献 68.5
Related Works of the Division of Chapter Structure
方法 作者 技术 特征 数据集 结果
传统机器学习方法 Athar[61] SVM, NB N-Grams,词性标签,词典,依赖结构,分句 ACL中8 736条引用句 Micro-F 75.5%;
Macro-F 47.1%
Athar等[52] SVM 正式引用,作者姓名,缩略语,引文列表,N-Grams等 ACL中8 736条引文上下文 Micro-F 80.4%;
Macro-F 68.7%
Abu-Jbara等[82] SVM 引文数,词性,自引,否定,依赖关系 ACL中14 000条引文上下文 Macro-F 71.4%
Xu等[88] SVM N-Grams,情感词典、结构特征 临床试验论文中4 182条引用句 Micro-F 86.0%;
Macro-F 71.9%
Ma等[83] SVM 一元组,极性分布,作者ID,H-index/P-index ACL中8 736条引文上下文 Macro-F 64.5%
Ikram等[89] SVM N-Grams,线索词 ACL中8 736条引用句;临床试验论文中4 182条引用句 F 85.4%
Amjad等[84] SVM, NB, DT, RF,
VerbNet
动词 ACL中8 736条引文上下文;ANN中701条引用句 A 90%左右
深度学习方法 Jochim等[90] 自动编码器 N-Grams DFKI中1 768条引用句,IMS中2008条引用句 Macro-F 54.9%
Munkhdalai等[85] 注意力机制, Bi-LSTM PubMed中5 000条引文上下文 F 76.0%
Ravi等[86] LSTM, CNN, Word2Vec N-Grams,依赖关系 ACL中8 736条引文上下文 F 69.4%
Yousif等[87] 多任务学习, CNN,
Bi-LSTM
ACL中3 568条引文上下文;ACL中1 368条引文上下文 F 88.3%
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