Content‐based citation analysis: The next generation of citation analysis

Ding, Ying; Guo, Zhen; Chambers, Tamy; Song, Min; Wang, Xiaolong; Zhai, ChengXiang

doi:10.1002/asi.23256

Cited by 190 publications

(142 citation statements)

References 72 publications

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“…The content-based techniques use content similarity techniques to measure the relatedness of two papers [12,13]. Using content to determine the similarity between research papers provides much better results compared to using only the citations.…”

Section: Related Workmentioning

confidence: 99%

Paper recommendation using citation proximity in bibliographic coupling

Habib¹,

Afzal²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Research paper recommendation has been a hot research area for the last few decades. Thus far, numerous different paper recommendation approaches have been proposed. Some of these include methods based on metadata, content similarity, collaborative filtering, and citation analysis, among others. Citation analysis methods include bibliographic coupling and co-citation analysis. Much research has been done in the area of co-citation analysis.Researchers have also performed experiments using the proximity of in-text citations in co-citation analysis and have found that it improves the accuracy of paper recommendation. In co-citation analysis, the similarity is discovered based on the frequency of co-cited papers in different research papers and those citing papers may belong to different areas. However, when proximity is used to calculate co-citation, the accuracy of recommendations improves significantly.Bibliographic coupling finds bibliographic coupling strength based on the common references between two papers. In bibliographic coupling, a large number of common references of two papers means that they belong to the same area, unlike co-citation analysis, in which there is a possibility that the citing papers may belong to different areas. Based on the observation that with the use of proximity analysis the accuracy in cases of co-citation analysis has improved, this paper investigates if the accuracy of paper recommendation can be further improved by using proximity analysis in bibliographic coupling. This paper proposes an approach that extends the traditional bibliographic coupling by exploiting the proximity of in-text citations of bibliographically coupled articles. The proposed approach takes into account the proximity of in-text citations by clustering the in-text citations using a density-based algorithm called DBSCAN. Experiments on a data set of research papers are presented to show that there is a substantial increase in accuracy of the recommendations produced by DBSCAN based on proximity analysis of in-text citations compared to traditional bibliographic coupling and content-based approaches.

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Section: Related Workmentioning

confidence: 99%

Paper recommendation using citation proximity in bibliographic coupling

Habib¹,

Afzal²

2017

Turk J Elec Eng & Comp Sci

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“…This is why simple counting of citations is often complemented with more complex content-based citation analysis. Giving an overview of content-based citation analysis at both syntactic and semantic levels, Ding et al (2014) emphasised that the main utility of such analysis is the interpretation of a citation's value in the context of the scientific paper rather than its simple frequency. Since current research articles are predominantly structured into sections, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Current impact of ceased journals: are they still alive?

Šember¹,

Škorić

Petrak³

2017

MJLIS

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“…Content analysis is a group of procedures to recollect and organized information in standard format to make inferences about its characteristics and meaning using manual or automatic methods (Ding, Zhang, Chambers, Song, Wang, and Zhai, 2014). This analysis could be automatic starting from a tagged corpus to build a model.…”

Section: Introductionmentioning

confidence: 99%

Towards the production of a corpus for content analysis of references in scientific literature

Hernandez¹

2014

Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC)

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Since, at the moment, there is not a goldstandard annotated corpus to allow generation and testing of automatic systems for classifying the purpose or function of a citation referenced in an article; it is necessary to build one, for this objective. The development of this kind of corpus is subject to two conditions: the first one is to present a clear and unambiguous classification scheme. The second one is to secure an initial manual process of labeling to reach a sufficient inter-coder agreement among annotators to validate the annotation scheme and to be able to reproduce it even with coders who do not know in depth the topic of the analyzed articles. This paper proposes and validates a methodology for corpus annotation for citation classification in scientific literature that facilitate annotation and produces substantial interannotator agreement.

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Content‐based citation analysis: The next generation of citation analysis

Cited by 190 publications

References 72 publications

Paper recommendation using citation proximity in bibliographic coupling

Paper recommendation using citation proximity in bibliographic coupling

Current impact of ceased journals: are they still alive?

Towards the production of a corpus for content analysis of references in scientific literature

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